This thesis presents a novel approach to make Artificial Intelligence (AI) more explainable by using a Belief Rule Based Expert System (BRBES). A BRBES is a type of expert system that can handle both qualitative and quantitative information under uncertainty and incompleteness by using if-then rules with belief degrees. The BRBES can model the human inference process and provide transparent and interpretable reasoning for its decisions. However, designing a BRBES requires tuning several parameters, such as the rule weights, the belief degrees, and the inference parameters. To address this challenge, this thesis report proposes to use a Differential Evolution (DE) algorithm to optimize these parameters automatically. A DE algorithm such as BRB adaptive DE (BRBaDE) and Joint Optimization of BRB is a metaheuristic that optimizes a problem by iteratively creating new candidate solutions by combining existing ones according to some simple formulae. The DE algorithm does not require any prior knowledge of the problem or its gradient, and can handle complex optimization problems with multiple objectives and constraints. This model can provide explainability by using different model agnostic method including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The proposed approach is applied to calculate Air Quality Index (AQI) using particle data. The results show that the proposed approach can improve the performance and explainability of AI systems compared to other existing methods. Moreover, the proposed model can ensure the balance between accuracy and explainablity in comparison to other models.