The rapid growth of online food delivery services has generated vast amounts of data that offer valuable insights. This data provides significant information about consumer behaviour, operational efficiency, and market trends. This study presents a comprehensive machine learning (ML) pipeline to analyse food delivery data, integrating demographic attributes, order details, and food preferences. The research includes data preprocessing, exploratory data analysis (EDA), regression modelling, hyperparameter tuning, and feature importance evaluation. Additionally, we applied advanced customer segmentation techniques to identify key consumer groups along with their distinct characteristics. The findings reveal crucial factors influencing delivery time, order value, and customer satisfaction. By leveraging predictive modelling and clustering algorithms, this study offers actionable intelligence for stakeholders to optimise service quality, manage inventory, personalize offerings, provide tailored menus, optimise discounts, and enhance operational strategies in the food delivery ecosystem.