Optimizing model performance involves hyperparameter tuning, regularization techniques, and effective data pipelines. Key strategies include adjusting learning rates, implementing dropout, and utilizing batch normalization. Data augmentation and efficient memory management enhance training, while profiling helps identify bottlenecks. TensorFlow.js tools facilitate these optimizations.