The human microbiome plays a critical role in health and disease, with recent innovations in microbiome research offering groundbreaking insights that could reshape the future of healthcare. This study explored emerging methodologies, such as long-read sequencing, culturomics, synthetic biology, machine learning, and AI-driven diagnostics, that are transforming the field of microbiome–host interactions. Unlike traditional broad-spectrum approaches, these tools enable precise interventions, such as detecting foodborne pathogens and remediating polluted soils for safer agriculture. This work highlights the integration of interdisciplinary approaches and non-animal models, such as 3D cultures and organ-on-a-chip technologies, which address the limitations of current research and present ethical, scalable alternatives for microbiome studies. Focusing on food safety and environmental health, we examine how microbial variability impacts pathogen control in food chains and ecosystem resilience, integrating socioeconomic and environmental factors. The study also emphasizes the need to expand beyond bacterial-focused microbiome research, advocating for the inclusion of fungi, viruses, and helminths to deepen our understanding of therapeutic microbial consortia. The combination of high-throughput sequencing, biosensors, bioinformatics, and machine learning drives precision strategies, such as reducing food spoilage and enhancing soil fertility, paving the way for sustainable food systems and environmental management. Hence, this work offers a comprehensive framework for advancing microbiome interventions, providing valuable insights for researchers and professionals navigating this rapidly evolving field.

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat that has outpaced the development of new antibiotics. Drug repurposing has emerged as a promising strategy for identifying alternative antimicrobial therapies. Antidepressants have attracted interest because experimental studies suggest they possess off-target antimicrobial activity, although growing evidence indicates they may also promote antimicrobial resistance. This review critically examines both the therapeutic potential and risks of antidepressant–microbial interactions. Methods: A structured narrative review was conducted using literature identified from major biomedical databases. Experimental, animal, microbiome, environmental, and clinical studies were synthesized according to antidepressant class, antimicrobial mechanisms, interactions with conventional antibiotics, microbiome modulation, and antimicrobial resistance. Results: Experimental evidence demonstrates that several antidepressants inhibit bacterial and fungal pathogens through mechanisms including membrane disruption, oxidative stress induction, efflux pump inhibition, and biofilm interference. Some agents also enhance the activity of conventional antibiotics. However, current evidence is largely preclinical. Multiple studies indicate that antidepressants may also promote antimicrobial resistance through reactive oxygen species-mediated mutagenesis, efflux pump activation, horizontal gene transfer, microbiome dysbiosis, and environmental exposure. Conclusions: Antidepressants represent both a promising drug-repurposing opportunity and a potential contributor to antimicrobial resistance. Although their antimicrobial properties are encouraging, current evidence remains predominantly preclinical, and clinically effective and safe antimicrobial dosing has not been established. At present, their antimicrobial use should be regarded as proof-of-concept rather than a clinically validated therapeutic strategy. Further pharmacokinetic, pharmacodynamic, animal, and clinical studies are required before routine antimicrobial application can be considered.