Neuroscience and artificial intelligence, the convergence of neuroscience and artificial intelligence has presented revolutionary sports performance optimization. This paper is a comprehensive Neuro. The AI system based on electroencephalography (EEG) signals and athletic predictive and enhancement deep learning architectures. performance with analytics of the brain. We developed a multi-level pipeline with the EEG signal acquisition, preprocessing Independent Component Analysis (ICA), neural biomarkers feature extraction (Sensorimotor) Classification using; rhythm, alpha, theta bands) and classification. Convolutional neural networks, Gated Recurrent Units (GRU). Transformer architecture, (CNN), and Transformer architecture. Our system achieved great precision with the Transformer model (98.1%), GRU model (97.8%), CNN model (94.3%), and 3–13% proves to be better than 70.5%-86.5% by 1.2%-1.8%. better than available state-of-the-art methods. The system distinguishes well between focused, fatigued, stressed and relaxed mental conditions having a time of inference less than 200 milliseconds, to render it appropriate to real-time applications. Cross-subject vali dation was accurate in 92.7% in 45 athletes of 5 sport. disciplines. Performance was illustrated by real-life case studies. increases of 6–12% in high performance athletes. This work establishes an effective architecture of sports science in the next generation, which allows. objective detection of mental fatigue, individual neurofeedback. performance forecasting, performance, and training.