Edge computing and multisensor integration have revolutionized sports training technologies, yet many existing systems remain computationally intensive and complex for everyday use. This study introduces an efficient and user-friendly intelligent sports training system that leverages edge-based multisensor data processing with lightweight neural networks. The system utilizes low-latency activity and real-time pose estimation data, powered by a modified MobileNetV2 model, to generate personalized training plans through reinforcement learning. Tests show that the system matches the accuracy of more complex models while significantly reducing computational needs. Compression techniques further enhance efficiency with minimal accuracy loss. User studies revealed notable improvements in fitness levels and adherence compared to traditional methods. The system adapts to real-time user performance, offering feedback and dynamically adjusting plans, with low energy consumption across mobile devices. This research shows that our developed system, which integrates multisensors and artificial intelligence, can make personalized sports training more accessible.