Evaluating and optimizing multi-modal models necessitates understanding data interactions and employing comprehensive metrics. Key techniques include hyperparameter tuning with Keras Tuner, utilizing transfer learning with pre-trained models, and implementing regularization to prevent overfitting. Visualizations like confusion matrices enhance model performance assessment.