<p>While microfluidic and microbial fuel cells represent promising solutions for clean energy generation and environmental sustainability, their performance depends on various design, material, and operational parameters. This paper presents a comprehensive and updated review of recent advances in the artificial intelligence (AI)–assisted optimization of microfluidic and microbial fuel cells. We systematically categorize and evaluate machine learning (ML) models, such as artificial neural networks, regression models, tree-based models, fuzzy logic systems, and reinforcement learning, highlighting their roles in predictive modeling and system analysis. Additionally, various optimization algorithms are reviewed, including evolutionary, swarm intelligence, physics-based, gradient-based, and policy-driven methods, with an emphasis on their integration with ML models to enhance fuel-cell performance. The optimization efforts are grouped into five key domains: (1) cell design parameters and operating conditions, (2) electrode parameters and design conditions, (3) fuel parameters and concentration conditions, (4) electrocatalyst and electrolyte composition, and (5) intelligent control strategies. The review also outlines future perspectives, including AI-guided automated fabrication, quantum computing to simulate complex electrochemical systems, and the integration of Internet of Things technologies for real-time, adaptive optimization. This review serves as a comprehensive reference for researchers aiming to leverage AI tools for the advancement of microfluidic and microbial fuel-cell technologies.</p>