Machine learning models are increasingly being deployed in complex decision-making scenarios, where both model performance and interpretability are important. Loss functions, which guide model optimization during training, are central to these models’ behavior. While performance metrics such as accuracy and F1-score are often prioritized, the influence of loss functions on interpretability has received less attention. Explainable AI (XAI) is crucial for building trust and ensuring the responsible deployment of machine learning models. While numerous XAI techniques exist, the impact of the chosen loss function on model interpretability remains under-explored. This research investigates the relationship between different loss functions (e.g., Mean Squared Error, Cross-Entropy) and the explainability of resulting models. The study investigates the impact of several popular loss functions–such as Mean Squared Error (MSE), Cross-Entropy Loss, and Hinge Loss–on both the performance and interpretability of machine learning models. The loss functions are applied to standard models (e.g., neural networks, support vector machines) and then evaluated their impact using accuracy, F1-score, and AUC. To assess interpretability, SHAP and LIME has been utilized to quantify model transparency and feature importance.