The prevalence of MDR bacterial infections is majorly influenced by human healthcare practices, societal behaviors, and veterinary contributions. Central to this crisis is the overuse and misuse of antibiotics in both clinical and agricultural settings. In human healthcare, antibiotics are often prescribed inappropriately, such as for viral infections where they have no efficacy, leading to the proliferation of resistant bacterial strains (Patel et al., 2022; Ventola, 2015). In addition, the prevalence of Hospital-Acquired Infections (HAIs) in long-term healthcare facilities (Yopa et al., 2023), with MDR pathogens like Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa demonstrating resistance to last-line antibiotics such as carbapenems (Mbimenyuy et al., 2023; Nigeria Centre for Disease Control, 2024). Resistance to other classes of antibiotics, including polymyxins and aminoglycosides, has further limited treatment options, leaving clinicians with fewer effective therapies (Sekar and Mythreyee, 2012; Tran et al., 2023). Compounding this issue are patient expectations, inadequate and sometimes lack of rapid diagnostic tools to aid in the proper use of antibiotics increasing acceptance of telemedicine by both healthcare providers and patients and systemic inefficiencies that pressure healthcare providers into unnecessary antibiotic prescriptions (WHO, 2017a; Sekar and Mythreyee, 2012; WHO, 2017b; World Health Organization (WHO), 2019).

Antimicrobial stewardship (ASP) refers to interventions designed to promote the optimal use of antibiotic agents, including drug choice, dosing, route, and duration of administration. ASPs are critical initiatives designed to optimize the use of antibiotics within healthcare settings, with the overarching goal of curbing the rise of AMR. Although, these have been documented to reduce resistance by 11–38% in high-income countries, there remain significant hurdles in LMICs (Baur et al., 2017). In sub-Saharan Africa, only 30% of countries have functional ASPs, constrained by diagnostic shortages and counterfeit drugs, which comprise 32% of antibiotics in Cameroon (Basco, 2004; Laxminarayan et al., 2013). In a rural Kenyan hospital, ASP implementation failed when clinicians, lacking resistance testing, reverted to outdated protocols, leading to treatment failures (Shamas et al., 2023). Weak enforcement, limited clinician training, and resource constraints underscore the need for a broader coordination framework to bolster stewardship efforts.

The role of veterinary practices in the MDR crisis is a critical but often underappreciated component of the broader clinical perspective. Antibiotics are widely used in animal husbandry, aquaculture, and veterinary medicine, not only for treating infections but also as a routine measure to enhance productivity. Veterinary antibiotic use, particularly as growth promoters, fosters resistant Escherichia coli, transferring to humans via food chains or direct contact (Van Boeckel et al., 2015). In Nigeria, livestock antibiotic use surged 40% from 2010–2012, driven by lax regulations, with farmers purchasing over-the-counter drugs without veterinary oversight (Adesokan et al., 2015). Manure runoff contaminates water sources, creating a critical One Health interface where veterinary practices amplify environmental resistance (Woolhouse et al., 2015).

Bacteria employ different mechanisms in achieving resistance ( Table 2 ). These include enzymatic degradation (e.g., beta-lactamases), target site alterations, efflux pumps, biofilms, and horizontal gene transfer, rendering treatments ineffective (Zhang and Cheng, 2022). Bacteria prevent antibiotic accumulation in their cells through limiting the entrance of drugs into bacterial cells. The porin channels in the outer membrane of the Gram-negative bacteria enables them to allow certain antibiotics such as like B-lactams and quinolones to penetrate the bacterial cells. Reduction in the number of bacterial porins could serve as means of hindrance against the antibiotics entering the cell thereby increasing the rate of resistance to the drugs (Darby et al., 2023). In addition, when the bacteria develop resistance to antibiotics through the modification of the ribosomal 30S or 50S subunits which in turn affects the production of protein. Some of the antibiotics that are resisted by this mechanism include: aminoglycosides, tetracycline, macrolides, chloramphenicol, lincosamides, and streptogramin (Sodhi and Singh, 2022; Sodhi et al., 2023).

These mechanisms of resistance driven by clinical and veterinary misuse, require integrated solutions. It is important to note that although clinical practices drive MDR, environmental reservoirs amplify its spread, necessitating an exploration of ecological pathways.

The environment functions both as a reservoir and a conduit for resistant bacteria and resistance genes. It provides a complex interface where microbial evolution, anthropogenic pollution, and public health intersect ( Table 3 ). However, the environment is often overlooked with very deleterious repercussions to public health. An exploration of the environmental dimensions of MDR bacterial infections emphasizes the pathways of the introduction of resistance, its maintenance and transmission throughout the environment. Within an integrated framework, it is very important to highlight environmental hotspots of AMR, the associated risks to human and animal health and their implications for surveillance and policy development.

Wastewater treatment plants, agricultural soils, and aquatic ecosystems serve as MDR reservoirs, perpetuating resistance cycles (Wang et al., 2022). In Brazil, pharmaceutical effluents contaminate rivers with blaNDM-1, while untreated irrigation water in the Middle East spreads resistant bacteria to crops (Alaali and Thani, 2020; Silva et al., 2024). Agricultural runoff, laden with antibiotics from manure, fosters resistance genes, impacting food and water safety across ecosystems (Ehsan, 2025). The aquatic ecosystem (rivers, lakes, ponds etc.) serves as reservoir of resistant pathogens with very high potential as re-entry points of these pathogens into human populations through irrigation, domestic water supply for drinking, recreational activities and food production lines. In addition, contaminated drinking water, crops, and aerosols expose humans to these MDR bacteria, particularly in LMICs with poor sanitation (Bjarnsholt et al., 2022). In India, 60% of urban water sources harbor resistant bacteria, directly linking environmental contamination to clinical infections (Kumar et al., 2020; Hamer and Coffin, 2021).

Mitigation strategies such as surveillance of resistance genes, stricter waste discharge regulations, and composting practices can reduce environmental AMR (Tsekleves et al., 2023). Public education campaigns on exposure risks, such as avoiding contaminated water sources, further strengthen prevention efforts (WHO, 2023).

Environmental spread of MDR bacterial infection is compounded by global dynamics, where travel (both human and animal migration) and inequities accelerate MDR’s reach. For instance, in 2018, 1.4 billion tourists facilitated the global spread of NDM-1 and mcr-1, with Saudi Arabia’s medical tourism linked to hospital-acquired infections (Payne et al., 2016; Arcilla et al., 2017). Cross-border patient transfers, such as between the Netherlands and Germany, face hygiene discrepancies, complicating infection control (Müller et al., 2015).

LMICs grapple with diagnostic shortages and counterfeit drugs, for example, 32% of antibiotics in Cameroon being substandard (Basco, 2004). Women, often caregivers or livestock handlers, face higher MDR exposure due to limited healthcare access, exacerbating gender disparities in treatment outcomes (Iskandar et al., 2020). In Rwanda, 55% of farmers use over-the-counter antibiotics, driven by poverty and lack of veterinary oversight (Manishimwe et al., 2017).

The main functions expected to be performed by the global health systems include: management of cross-border externalities, mobilizing global support for disadvantaged populations, performing oversight functions on the overall performance of the system and ensuring that adequate global public goods are provided (Moon et al., 2017) ( Table 4 ).

The WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) tracks resistance patterns, but only 20% of sub-Saharan African nations fully participate due to resource constraints (WHO, 2023). FAO and WOAH advocate phasing out antibiotic growth promoters, yet enforcement varies, with 50% of LMICs lacking national action plans (FAO, 2024) ( Table 5 ).

Surveillance bottlenecks such as underreporting, limited laboratory capacity (only 25% of African hospitals are equipped for resistance testing), and poor data interoperability hamper global surveillance efforts (WHO, 2023). Nigeria’s National Action Plan for AMR (NAP-AMR) highlights that 50% of its labs lack resistance testing capabilities, delaying timely interventions (Nigeria Centre for Disease Control, 2023a). These global challenges underscore the need for a One Health approach, integrating human, animal, and environmental strategies to combat MDR effectively.

MDR bacteria cycle bidirectionally between humans, animals, and environments, with 60% of human pathogens being zoonotic, such as Escherichia coli and Salmonella (Klous et al., 2016). For example, in Ghana, ESBL-producing E. coli shared between poultry and farmers highlights occupational risks, driven by close contact in agricultural settings (Falgenhauer et al., 2019).

operates through national task forces, as exemplified by Nigeria’s NAP-AMR, where human, veterinary, and environmental ministries collaborate on surveillance and policy (Nigeria Centre for Disease Control, 2023a). Inter-ministerial platforms facilitate data-sharing, reducing response times and aligning interventions across sectors (Liu et al., 2016).

In 2015, China’s identification of the mcr-1 gene in livestock sparked global concern, as it spread via international trade (Charoenboon et al., 2019). A 2017 ban on colistin as a growth promoter reduced its prevalence by 40%, demonstrating the power of One Health-driven policy interventions (Charoenboon et al., 2019). Operational integration paves the way for comprehensive One Health strategies and this is evidenced by global successes.

The dynamic relationships within the ecosystem ( Figure 2 ) depicts the interactions between humans, animals and the environment and how any misuse of or release of antibiotics (inputs such as antibiotic misuse, livestock antibiotics, runoff (water droplet) disrupts the equilibrium the entire system through the release of deleterious outputs in this case-resistant pathogens via water, food and contact with negative impact.

Thailand’s 2017–2021 National Strategic Plan reduced antibiotic use by 30% through regulatory reforms (Charoenboon et al., 2019). The EU’s surveillance network lowered resistance rates by enhancing data-sharing (Authority and EMA, 2021). Brazil’s 2024 AMR plan strengthened intersectoral coordination (Ministério da Saúde (Brazil), 2018). Nigeria’s NAP-AMR improved surveillance coverage (Nigeria Centre for Disease Control, 2023b). And India’s Red Line campaign cut antibiotic misuse by 20% (Ranganathan and Ranjalkar, 2023) ( Figure 3 ).

These diverse, well-documented successes ( Table 6 ) inform scalable One Health models. Brazil’s intersectoral task forces exemplify effective coordination, sharing data through joint risk assessments (Ranganathan and Ranjalkar, 2023). Ghana’s Antimicrobial Awareness Week, reducing misuse by 15%, demonstrates the power of community-driven initiatives to complement policy efforts (Tsekleves et al., 2023).

The application of AI to manage AMR is fast gaining ground (Mansur et al., 2023). The potential for integrating technology (e.g., AI, Machine Learning (ML)) for real-time MDR monitoring and tailored interventions can be achieved through various ways to enhance effective healthcare delivery. The integration of AI and ML in diagnostics and treatment planning revolutionizing patient care, particularly ML, in medical diagnostics is ushering in a new era of healthcare. This revolutionary approach is characterized by increased accuracy, efficiency, and speed, surpassing traditional human capabilities in certain aspects.

Giving practical examples, in Ethiopia’s rural clinics, a young mother’s life was saved when WHO’s 2024 AMASS tool identified a resistant Escherichia coli infection in hours, cutting diagnostic time by 50% and guiding precise therapy (Martin, 2021). AI’s Machine Learning capabilities, as demonstrated by IDseq’s global deployment, analyze genomic and clinical data to predict resistance patterns with 90% accuracy, revolutionizing MDR management (Luca, 2024).

However, challenges persist: algorithm bias from unrepresentative datasets, non-standardized data inputs, and infrastructure gaps in LMICs—where only 10% of laboratories have cloud connectivity—limit scalability (Tran et al., 2023). Ethical concerns, such as patient data privacy and equitable access, further complicate adoption, necessitating robust governance frameworks (Schar et al., 2021) Despite these hurdles, AI’s transformative potential, when paired with investments in LMIC infrastructure, offers a pathway to precision interventions and global health equity.

Community-driven initiatives, such as Ghana’s Antimicrobial Awareness Week (15% misuse reduction) and India’s Red Line campaign (20% reduction), demonstrate the power of public education (Tsekleves et al., 2023; Sanga et al., 2024). Women, often primary caregivers and livestock handlers, face heightened MDR exposure due to limited healthcare access, requiring targeted education to address gender disparities and reduce transmission risks (Iskandar et al., 2020).

Global bans on antibiotic growth promoters, public-private funding models, and standardized surveillance systems, as implemented in Brazil’s 2024 AMR plan, are essential for sustainable progress (Ranganathan and Ranjalkar, 2023). Nigeria’s NAP-AMR, emphasizing laboratory upgrades, provides a replicable model for LMICs to strengthen diagnostic capacity (Ministério da Saúde (Brazil), 2018). Clinical misuse, environmental reservoirs, and global spread, particularly in LMICs, thrive in the absence of coordinated responses. Thailand’s 30% reduction in antibiotic use through policy reform and AI’s 50% improvement in diagnostic speed offer beacons of hope, yet gender inequities and surveillance gaps persist (Authority and EMA, 2021; Martin, 2021).

We present a flowchart of an AI workflow from data input (EHR, lab results) to ML analysis (AMASS, IDseq), resistance prediction, clinical guidance, and policy feedback ( Figure 5 ). This will enable rapid MDR detection, critical for precision interventions, prompt community engagement and policy development.

These strategies converge in a bold vision for global health security, as outlined in our concluding call to action.