Online travel platforms offer a wide range of vacation rentals, making it difficult for users to choose the best option based on quality and reliability. This work presents a machine learning framework that predicts the quality rating of vacation rentals using factors such as location, price, amenities, host information, and customer reviews. The model analyses past data to identify patterns and generate accurate rating predictions. In addition to prediction, the framework explains how different features influence the final rating, helping users understand the reasons behind each score. This (Random Forest with SHAP (Shapley Additive Explanations). ) approach supports travelers in making better booking decisions and helps property owners improve their services, leading to enhanced user satisfaction on online travel platforms.