This study applies spatiotemporal data mining and machine learning techniques to analyze New York City’s 311 service request data. A 10-million-record subset was extracted from the full 42-million-row dataset and cleaned, standardized, and feature-engineered. Exploratory data analysis revealed key trends in complaint distribution across boroughs, time, and socioeconomic factors. Advanced data mining techniques including geospatial KMeans clustering, contrast and sequential pattern mining, association analysis, and anomaly detection uncovered localized hotspots, co-occurring complaint behaviors, and event-driven surges. Time-series forecasting and XGBoost-based regression were used to predict complaint volume and resolution time. The findings demonstrate how large-scale civic data can inform proactive urban service planning.