In the green motor vehicle era, fuel cell hydrogen electric vehicles (FCHEVs) are becoming promising alternatives. Thus, ensuring the proper operation of FCHEVs solely depends on advanced energy management systems (EMS). In this light, this work looks deeply into how combining machine learning and physics-based models can make FCHEVs operate effectively through improved EMSs. This study extensively analyzes how machine learning and physics-based models operate together in FCHEV-EMS. It therefore breaks through existing research and identifies insights, challenges, and potential future directions. It also looks closely at how machine learning meets challenges in adapting to real-time and handling changing conditions. To gain a better understanding of these issues, this study further recommends innovative ways to integrate machine learning flexibility within the precision of physics-based modeling. It therefore reveals an intriguing potential for additional study in the world of FCHEV-EMS. It represents the integration of machine learning and physics-based models as a potent technique to deal with EMS difficulties and accelerate advances in FCHEV energy management. In the end, it outlines significant findings, addressing why this integrated strategy is crucial in making FCHEVs a world-leading sustainable means of transportation. Through its comprehensive review and strategic perspectives, this initiative aims at catalyzing innovations that actively contribute to the sustainable advancement of FCHEVs.