AI technology for vehicle modules in heterogeneous network environments represents a critical advancement in modern automotive systems. As vehicles evolve from simple mechanical systems to complex digital platforms with up to 150 ECUs (Electronic Control Units) connected through various communication protocols, efficient on-vehicle data processing has become essential. This comprehensive investigation examines heterogeneous network architectures, edge AI hardware and software components, integration methodologies, and applications across vehicle modules including ADAS (Advanced Driver Assistance System), infotainment, vehicle health monitoring, and autonomous driving. Technical challenges in resource-constrained environments are identified alongside solutions for performance optimization, real-time processing, system compatibility, and security. Current research trends and future directions provide valuable insights for automobile manufacturers, parts suppliers, and researchers, contributing to the advancement of autonomous driving and connected car technologies through effective integration of heterogeneous networks and edge AI.