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This comprehensive end-to-end machine learning pipeline predicts food delivery order status (Delivered, Delayed, Cancelled) from operational features, using a complete workflow from ZIP file extraction through EDA, feature engineering, and dual-model training with Logistic Regression and Random Forest. The analysis reveals that the dataset is synthetically generated with no predictive signal (both models score ~33% accuracy at random-chance baseline), supported by near-zero feature correlations and detailed visualizations of delivery time distributions, temporal patterns, and feature importance—concluding with actionable business recommendations for reframing as binary classification, enriching the feature set with real-world operational data (traffic, prep time, weather), and adopting time-based cross-validation for genuine deployment readiness.