The convergenceConvergence of Artificial IntelligenceArtificial Intelligence (AI) (AI) and Synthetic BiologyAI and synthetic biology convergence (SB) offers transformative potential in healthcare and agriculture but introduces significant ethical, social, and ecological challenges. This chapter comprehensively analyzes these dualities, addressing critical issues such as data privacy violations, algorithmic bias exacerbating inequities, diluted accountability in complex systems, adverse socioeconomic impacts on vulnerable populations, and environmental integrity concerns. In healthcare, AI-driven diagnostics and personalized therapies raise concerns over patient data security, equitable access, and the opacity of decision-making. In agriculture, AI-SB innovations challenge farmer data ownership, socioeconomic fairness, and ecological safety. To navigate these complexities, the chapter advocates for robust, adaptive governanceGovernance solutions grounded in interdisciplinary collaboration, stakeholder participation, and the foundational TAPIC principles (Transparency, Accountability, Participation, Integrity, Capacity). It presents a structured framework comprising eight core governanceGovernance pillars—spanning enhanced data protection, algorithmic transparency, ethical protocols for AI-generated biological systems, ecological impact evaluation, and collaborative pilot programs. These pillars are operationalized through five key data governanceGovernance domains (privacy, sharing, standardization, benefit-sharing, monitoring). This comprehensive, proactive approach aims to ensure that AI-SB innovations are developed and deployed responsibly, aligning technological advancements with societal values and mitigating risks to foster an equitable, sustainable, and just future.