Food packaging has evolved from a simple way to an active system that can monitor, communicate, and respond to shifts in food quality and environmental conditions. Conventional packaging technologies are only effective in preserving food, but they are not able to provide real-time access and monitoring of packed food (Sagar et al., 2022a). The evolution of AI-powered food packaging has brought a huge change in the food sector. It authorizes users to access data-driven solutions that are directly associated with quality control, food safety improvement, and traceability (Huang and Wang, 2025).

Conventional plastic packaging has been a key part of the market since its inception. However, the traditional packaging is responsible for landfill waste and significant pollution. Mainly, food packaging consists of plastics (40%), followed by paperboard (31%), metals (15%), glass (9%), and 3% other materials (Sarkar and Aparna, 2020). The most commonly used non-biodegradable packaging ingredient is plastic, which causes several environmental issues and risks to human health and this has shown the path of sustainable alternatives like biodegradable polymers and smart packaging (Hassoun et al., 2023). In addition, traditional food packaging is not able to maintain and monitor the condition of food in a real sense, which results in substantial food spoilage and waste (Sundaresan et al., 2025). Thus, there is an instant need for “smart” food packaging solutions that can provide real-time data on location, origin, food quality, and product conditions. A sustainable future with a better economy, environment, and society can be established by addressing the problem of food waste.

The current development of smart food packaging, includes edible, active, intelligent, and biodegradable systems (Asiri et al., 2024). However, most solutions today rely on static indicators or separate sensing technologies. Challenges like temperature abuse, microbial spoilage, food fraud, and pollution from non-biodegradable plastics are still unresolved (Khan et al., 2024). Additionally, the lack of data-driven decision-making limits the effectiveness of traditional smart packaging.

AI-driven smart packaging integrates technologies, including sensors, blockchain, and machine learning. It offers real-time freshness detection, alerts for contamination, and better predictions for shelf life (Sundaresan et al., 2025). These novel approaches build consumer trust and help reduce food waste by monitoring packaging conditions and ensuring accurate inventory management in the entire supply chain. With the demand for smart, safer, and more sustainable food packaging solutions, AI-powered technologies are creating a transparent and more efficient food system (Onyeaka et al., 2023). Therefore, combining AI with smart food packaging helps maintain a better food supply chain by improving traceability and reducing food waste. The evolution of smart packaging technologies, mainly active and intelligent forms, marks a significant step in food preservation and safety. Active packaging extends shelf life and ensures food safety by maintaining the environment around the packed foods. Intelligent packaging includes sensors that provide real-time data about various quality attributes of product (Chen et al., 2020). Researchers are also exploring AI-enabled biodegradable packaging technologies to reduce reliance on non-biodegradable plastics and support a sustainable future.

The present review discusses smart food packaging and the scope of AI-driven food packaging. It also exhibits the latest trends, challenges, and future directions in AI-enabled smart food packaging. It discusses how AI-enabled smart packaging works with machine learning, blockchain, and IoT technologies. These combinations can change food safety, quality assurance, and supply chain logistics. Unlike descriptive reviews, this study focuses on mechanistic insights, compares different approaches, and identifies future research needs. It aims to support the creation of scalable, sustainable, and smart food packaging systems.

Scopus, Web of Science, and Google Scholar were used as the key databases to analyze, collect, and download the relevant literature for this review from the period of 10 years (2015-2025), emphasizing recent advancements in AI-enabled smart packaging domains. “Artificial intelligence,” “smart food packaging,” “AI sensors,” “Internet of Things,” “blockchain traceability,” “food safety,” “machine learning,” and “sustainable packaging” were selected keywords utilized to search the studies. Only peer-reviewed journal articles, book chapters, and high-impact conference proceedings were included. Experimental findings, new applications, or critical reviews were prioritized to be included. Duplicate or unrelated records were removed. This approach promotes transparency and reliability while covering the most relevant insights.

Nearly one-third of all food produced globally goes to waste, which is enough to feed 2 billion people. At the same time, millions fall sick from foodborne illnesses each year (Guillou and Matheron, 2012). The current food packaging systems work, but are not smart enough to solve these problems. The traditional packaging system cannot reveal when food is actually spoiled, track its journey accurately, or adjust to changing conditions (Mahalik and Nambiar, 2010). This is where AI plays a key role. By adding intelligence to packaging with tiny sensors that detect spoilage, blockchain systems that track every step from farm to table, and algorithms that accurately predict shelf life, a significant reduction in food waste, improved safety, and real-time transparency can be achieved (Huang and Wang, 2025). Imagine milk cartons that alert you before they spoil or grocery stores that automatically discount items nearing expiration. This is not just a vision for the future; it is a practical solution that the food industry needs today. However, AI’s role extends beyond convenience. It is becoming crucial for our planet and health. As food fraud becomes harder to detect, climate change makes it more difficult to manage supply chains and meet changing consumer needs around the world. AI helps packaging think for itself by predicting contamination risks before outbreaks occur, optimizing shipping routes to cut energy use, and designing eco-friendly materials that biodegrade more effectively (Mu et al., 2024). For businesses, this means fewer recalls and higher profits. For consumers, it provides safer meals and less guilt about waste. In a world where every bite counts, AI-powered packaging is not just smart, it is responsible.

Smart food packaging technologies mainly involve active packaging, intelligent packaging, and biodegradable packaging. These techniques are utilized in the food industry at various levels to ensure the safety, quality, and timely transportation of food products.

AI-enabled smart packaging has significant scope for evaluating performance by correct parameters, such as detection accuracy, prediction error, robustness, and latency. As an example, image-based deep learning classifiers have achieved ∼97.8% accuracy in assessing fruit freshness in controlled conditions. However, detection accuracy may decrease by 5%–20% in real-world situations because of changing lighting, reflections, and variations in fruit types (Li Q. et al., 2023). Predictive models, such as RFID and TTI systems, have made real-time cold-chain monitoring easy and efficient by decreasing spoilage up to 20% in dairy pilot plants (Abekoon et al., 2024). MAP combined with AI to predict the real time shelf-life of dates. The results showed strong findings with 39 ± 3.34 days shelf life at 5 °C, where ML models accurately estimate remaining shelf life (Kumar et al., 2025). However, issues like sensor drift, environmental changes, and data bias remain. These challenges highlight the need for standard validation protocols that report sensitivity, specificity, ROC-AUC, RMSE, and cost metrics across various geographical datasets (Gupta et al., 2025).

AI is transforming the food packaging industry by enabling fast, real-time, and precise monitoring of freshness. This helps minimize food waste and supports a sustainable packaging system. Deep learning strategies like convolutional neural networks (CNNs) have shown accurate results. These models can differentiate fresh and decayed fruits up to 97.82% accuracy using image processing (Palakodati et al., 2020). Guo et al. (2020) made a combination of colorimetric barcode sensors and CNNs to evaluate the freshness of meat without destruction. Kazi and Panda (2022) used residual CNN architectures to improve freshness classification. Mohi-Alden et al. (2022) prepared a real-time grading method for bell peppers that uses CNNs to sort them based on maturity and size. ML models, including artificial neural networks (ANN), random forests, and support vector machines (SVM) have been utilized to evaluate the freshness of beverages, mushrooms, and meats with 80% accuracy (Anil et al., 2019; de Santana et al., 2019). Harnsoongnoen and Jaroensuk (2021) used density-based computer vision to classify egg freshness. Using AI with sensor systems like piezoelectric sensors and pH-sensitive colorimetric films has strengthened the evaluation of non-invasive freshness (Ezati et al., 2019). However, inconsistent sensor performance, high data processing demands, and scalability issues are the remained challenges. There, the researchers are exploring chipless RFID tags, flexible printed sensors, and edge computing for cost-effective smart packaging to tackle these issues (Feng et al., 2014; Wang et al., 2019). These AI-packaging systems hold great promise for the future of food quality assurance and sustainability. Table 1 comprises recent AI innovations in food packaging.

Apart from that, there are several commercial smart food packages available in the market that ensure food safety and quality in cold chains. Systems like 3M™ MonitorMark®, WarmMark®, and TempDot® offer clear visual signals for temperature breaches through dye migration or color shift mechanisms. Time-temperature indicators such as OnVu™, CoolVu Food®, and Keep-it® use UV-activated or fading inks to show total exposure. Others like Freshtag®, eO®, and TOPCRYO combine enzyme or microbial sensing for freshness and spoilage detection. Traceo®, Cold Chain iToken™, and Tempix® improve traceability with barcode-enabled or dynamic shelf-life monitoring. These tools support safe logistics for perishable goods (Table 1).

The combination of AI with smart packaging is changing the food industry from a conventional packaging approach to a future of intelligent solutions. This powerful integration is revolutionizing the way we monitor food quality, optimize complex supply chains, and promote sustainability. From the field to the kitchen, AI is maintaining significant control in food spoilage and the equal distribution of fresh food across the globe. This technology is being applied not only to the strict cold-chain logistics in North America and Asia for perishables, but also to European nations for ensuring food safety and real-time traceability (Table 2).

In the important field of freshness monitoring, AI has changed a subjective guess into a precise science. For fruits, image analysis using Convolutional Neural Networks (CNNs) can now classify freshness with over 97.8% accuracy by analyzing surface images (Palakodati et al., 2020). In the same way, for packaged meats, smart labels with colorimetric barcodes are read by Deep CNNs (DCNNs) to predict microbial spoilage with classification rates as high as 99.2% (Guo et al., 2020). These systems can detect food starting to spoil 24–48 h before we can notice it, providing an important early-warning system (Li X. et al., 2023). This is especially important for managing perishables like seafood and dairy in areas with strict safety standards, such as Southeast Asia, Europe, and North America.

This intelligence extends to the production line, where AI-powered visual inspection systems are now outperforming human workers. Using deep learning models trained on datasets of over 100,000 annotated images, these systems can inspect and sort thousands of food items per minute, identifying defects and mold with over 95% accuracy (Abekoon et al., 2024). This high-tech sorting has been shown to cut food waste at this initial stage by up to 50% compared to manual methods (Bhargava and Bansal, 2021). The value of this is immense in major agricultural hubs like India, China, and Latin America, where a significant portion of the harvest has traditionally been lost after picking due to inadequate inspection (Li X. et al., 2023; Kumar et al., 2025).

Furthermore, AI is bringing a new level of predictability and optimization to the entire supply chain. By analyzing data from RFID and Time-Temperature Indicators (TTIs), AI models can now predict a product’s remaining shelf life with an accuracy within ±5% (Gupta et al., 2025). This allows for “dynamic expiry dating,” where labels can provide a more realistic ‘use-by’ date, a practice proven to reduce retail food waste by 20%–30% in pilot studies (Prakash et al., 2025). AI is also being applied to reroute shipments (dynamic) to control cold chain outbreaks by 15% (Kumar et al., 2025). The Middle Eastern and Gulf markets are quickly adopting such sophisticated tracking and prediction techniques for high-value products like premium meats and dates, which require sophisticated Modified Atmosphere Packaging (MAP) to support lengthy transits.

On the front lines of food safety, AI is merging with cutting-edge biosensor technology to create a powerful defense against pathogens. New biosensors, including CRISPR-Cas-based prototypes, can detect dangerous bacteria at incredibly low levels (10–100 CFU/mL) in just 2–4 h, a process that used to take 24–48 h with traditional cultures (Nayak and Dutta, 2023). AI is essential here for interpreting the complex, multiplexed data from these sensors, achieving a 99.9% specificity in identifying pathogens (Abekoon et al., 2024). This rapid, ultra-sensitive detection is a game-changer for global food exporters, for whom a single safety lapse can lead to devastating trade bans and recalls.

Finally, AI is actively driving the sustainability revolution in packaging materials themselves. Machine learning is accelerating the development of new, high-performance biodegradable polymers, reducing R&D cycle times by 40%–60% (Prakash et al., 2025). Predictive models have already suggested material blends that achieve a greater than 25% reduction in oxygen transmission rate compared to conventional bioplastics. While these advanced, AI-optimized biodegradable systems are first taking hold in developed regions, more affordable solutions like low-cost printed freshness indicators are providing a vital stepping stone for lower-income areas such as Sub-Saharan Africa, ensuring that the benefits of smarter packaging can eventually reach everyone (Gupta et al., 2025). This aligns with the global imperative to tackle the 1.3 billion tonnes of food wasted annually.

The primary advantage of AI-based smart packaging is its potential to detect early spoilage signs. This helps reduce food waste and improves safety. By using sensors with AI, we can better predict shelf life and provide clear information to consumers. This leads to better inventory management and fewer losses for retailers and households (Nayak and Dutta, 2023; Li X. et al., 2023). The other advantage is better traceability. AI-enabled detection and systems exhibit fixed records of food supply. This helps cut down on food fraud and boosts consumer confidence, especially for high-value items like seafood and specialty meats (Abekoon et al., 2024). However, these systems have their drawbacks. The high cost of smart tags or sensors, like RFID tags that cost about $0.20 to $0.50 each, along with integration costs, makes it difficult for low-margin producers to adopt them (Prakash et al., 2025). Issues, such as sensor durability, the risk of nanomaterials entering food, and the lack of consistent global regulations also slow down commercialization (Nayak and Dutta, 2023). Additionally, cybersecurity and privacy linked to cloud-based monitoring technology may show concerns mainly in weak infrastructure areas.

AI-enabled smart food packaging represents an important step forward in addressing food safety, quality assurance, and sustainability issues. Combining AI with sensors, machine learning models, IoT platforms, and blockchain technologies allows for real-time spoilage detection and accurate shelf-life predictions. It also helps track the food products across the food supply chain. These innovations help reduce food waste, build consumer trust, and improve regulatory compliance.

However, challenges, such as high sensor costs, scalability, data standardization, cybersecurity, and regulatory approval limit the adoption. Current research mainly focuses on physical spoilage signs, while chemical contaminants, allergens, and nutritional decline are not fully explored.

Future research should focus on creating cost-effective, biodegradable, and printable sensors, chipless RFID technologies, flexible electronics, and self-learning AI algorithms. Collaboration among food scientists, material engineers, and data scientists is crucial to develop next-generation smart packaging systems that are intelligent, sustainable, and centered on the consumer.