Unveiling the Multifaceted Landscape of Biohacking: Insights From a Machine Learning-Driven Systematic Review

Biohacking represents an emerging interdisciplinary phenomenon at the intersection of biotechnology, citizen science, and human enhancement, yet its complex landscape remains inadequately characterized. This study presents a systematic review methodology that combines PRISMA-guided study selection with a subsequent machine learning analytical pipeline to comprehensively analyze and classify biohacking's multifaceted dimensions. We developed an innovative multi-objective optimization framework combining FOX algorithm —specifically adapted for balanced topic extraction— and NSGA-II algorithms to ensure balanced topic extraction with optimal semantic quality and distributional equity. Through Latent Dirichlet Allocation enhanced by our optimization approach, we analyzed peer-reviewed articles from 2020-2025, revealing three distinct thematic domains: grassroots DIY experimentation, institutional health research, and biotechnological laboratory innovation. The study employed Gradient Boosting with four transversal themes—Ethics and Regulation, Social and Cultural Impacts, Sustainability, and Technology—as predictive features, demonstrating the feasibility of automated classification in this emerging field. LIME analysis revealed polarized contributions along a Social-Ethics continuum, with DIY approaches prioritizing community engagement while institutional practices emphasize regulatory compliance. Critical findings include the systematic absence of environmental sustainability considerations across all domains and fundamental tensions between community-driven innovation and institutional regulation. This research identifies biohacking as a complex ecosystem characterized by empirically identifiable organizational patterns, providing an evidence-based foundation for adaptive governance frameworks. The methodological-applicative contribution lies in demonstrating that machine learning-driven review enables objective quantification and reproducible analysis of emerging interdisciplinary fields—where the value resides not in scalability but in measurement precision and replicability—while the biohacking application generates substantive insights for evidence-based policy development. Our findings highlight the need for integrated approaches that balance technological innovation with ethical oversight, addressing the critical sustainability gap while managing the tension between democratized science and public safety.