This study focused on identifying emotional states by analyzing brain and physiological data, aiming to improve diagnostic and intervention strategies. Sixteen patients watched videos that elicited positive, negative, or neutral emotions, and they rated these on a 9-level valence scale. The study collected data from three sources: BOLD fMRI signals, PPG (photoplethysmography), and respiratory data across 30 trials of 25 s each. A multi-expert ensemble system was used to predict valence ratings from these physiological signals, employing advanced AI models tailored to each data type. For fMRI data, graph representations were created to capture neural activity, which were then processed using a Graph Attention Network (GAT). PPG and respiratory signals were analyzed using a sliding window approach, focusing on changes from a baseline to identify significant features. These features were processed using Fully Connected Networks (FCN). The models were trained using nested Leave-One-Subject-Out cross-validation, and their predictions were combined using a meta-learning approach, which involved training an additional AI model (either Random Forest or FCN) on the outputs of the initial models. The study demonstrated the feasibility of a multi-expert meta-learning approach for emotion detection from multi-modal physiological data, showing promise for enhancing accuracy in emotional state analysis. Further optimization of single-modality models is expected to improve performance.