Phytochemicals from Edible and Medicinal Plant as Multi-Target Agents Against Multidrug-Resistant Pathogens: Mechanistic Insights, Prospects, and Challenges

Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa rank among the most challenging pathogens due to increasing prevalence of multidrug-resistant (MDR) strains. These pathogens pose major risks to public health and food safety, earning their inclusion on the World Health Organization (WHO) priority list of MDR bacteria. While available conventional antibiotics are becoming less effective, natural products from plant extracts offer promising alternative and synergetic effects that can restore efficacy and lower required doses. Their antimicrobial activity is attributed to phytochemicals such as phenolic compounds and terpenoids acting via membrane disruption, efflux pump inhibition, biofilm interference, and cell protein disruption. Furthermore, phytochemicals in essential oils, such as carvacrol, thymol, and cinnamaldehyde, also exhibit antimicrobial and antioxidant activities. Their broad antimicrobial effects extend shelf life and enhance food safety, making them effective natural alternatives to synthetic preservatives. Moreover, advances in extraction and characterization techniques, including green solvents, spectrometry and hyphenated chromatographic methods, have improved recovery, identification and quantification. In addition, artificial intelligence (AI) emerges as a transformative tool to accelerate discovery, optimize compound screening, and predict synergistic interactions. Notwithstanding these advances, challenges persist in standardization, bioavailability, and clinical translation. Further studies are needed to isolate active compounds, elucidate mechanisms of action, validate combined use with conventional antibiotics and overcome formulation, delivery, sensory and regulatory hurdles. This review examines current knowledge of opportunities and limitations of plant-based antimicrobials against MDR pathogens supported by advances in extraction, characterization, and AI.