The article discusses the advancements in Air-Cooled Open Cathode Proton Exchange Membrane Fuel Cells (AO-PEMFC) and the optimization of these fuel cells through machine learning techniques. The AO-PEMFC technology is highlighted for its potential in various applications such as power supply for unmanned aerial vehicles and mobile power generation equipment. The optimization of fuel cell parameters using data-driven machine learning approaches like Support Vector Regression (SVR) and Gaussian Process Regression (GPR) is emphasized for its efficiency and interpretability. Recent research has led to breakthroughs in key areas of AO-PEMFC design. Structural improvements such as annular flow channels and optimized cathode channel bending designs have significantly enhanced mass transfer efficiency and thermal management capabilities. The article also mentions the benefits of 3D wave channels, bionic flow fields, and vertical flow designs in increasing power density. Future trends in AO-PEMFC research focus on intelligent algorithm-driven multi-objective optimization, durability enhancements in extreme conditions, and the development of low-cost mass production processes for complex flow channels. The article concludes by highlighting the suitability of AO-PEMFC technology for long endurance power in UAVs, portable energy sources, and automotive auxiliary systems, indicating a promising engineering prospect.