Emotion recognition helps in many areas like healthcare, interaction with computers, and smart settings. Even so, the methods currently in use are limited by small amounts of data covering several sensors, issues with applying them to various data sets, and lacking clear explanations, which makes people question their decisions. The framework developed in this research combines Federated Meta-Learning (FML) and Explainable AI (XAI) to boost accuracy, make it adaptable, and keep it transparent. It combines EEG, ECG, GSR, speech, and facial images and then isolates their features with VMD, wavelet transforms, and deep learning. This learning approach makes it possible for separate datasets to be trained and access each other’s results without sharing private data. Using SHAP and LIME in explanatory AI techniques makes it easy to understand and trust AI actions. As a tool for monitoring health and driver fatigue instantly, the framework is better than older systems and provides dependable and scalable recognition of emotions.