Machine learning (ML) model interpretation tools are essential for understanding the decision-making processes of ML models, which often function as "black boxes." These tools help assess the trustworthiness and reliability of model predictions, crucial for applications where decisions have significant consequences. Model interpretation shifts the focus from simply looking at outcomes to understanding the reasoning behind predictions, enhancing aspects such as fairness, reliability, causality, and trust among stakeholders. There are various methods for interpreting ML models, categorized into model-specific and model-agnostic approaches, which can be applied to both local and global prediction scopes. Some prominent interpretation tools include ELI5, LIME, SHAP, and MLXTEND, each offering unique features for analyzing models' behavior. These tools can explain both individual predictions and the overall model behavior, using techniques like feature importance, local approximations, and Shapley values. They are valuable for ensuring models are interpretable, fair, and trustworthy, especially in complex decision-making scenarios.