Efficient traffic flow analysis and travel management in unfamiliar urban areas remain critical challenges in intelligent transportation systems. This study presents an intelligent system based on vehicle license plate recognition capable of collecting spatiotemporal data and analyzing driver movement patterns. Leveraging image processing and machine learning techniques, the system processes vehicle passage data and provides context-aware recommendations for travel and traffic management. License plate recognition achieved an accuracy of 98.98% using advanced YOLO models, while clustering and classification of movement patterns predicted driver behavior with 95% accuracy. The research was conducted in four phases: phase 1: license plate detection and data collection, phase 2:data processing with machine learning, phase 3: design of the intelligent recommendation system, and phase 4: system performance evaluation. The feedback from 20 users indicated an average satisfaction score of 4.5/5, demonstrating the system’s effectiveness in delivering precise and personalized recommendations for travel management in unfamiliar areas.