The “Intelligent Traffic Optimization System” is a state-of-the-art system leveraging machine learning techniques to enhance urban mobility and infrastructure management. The system implements and compares various machine learning models including Random Forest, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), utilizing a comprehensive dataset of traffic flow measurements. Using a dataset of 10,000 entries with 20 features including temporal, spatial, and environmental factors, the models were evaluated extensively. Results showed the LSTM model achieved highest accuracy at 95.3% with an F1 score of 95.8%, followed by CNN at 93.1% accuracy and 93.8% F1 score. Random Forest and SVM achieved 90.2% and 88.4% accuracy respectively. This research suggests the importance of deep learning approaches in traffic flow prediction and congestion detection, contributing significantly to intelligent transportation systems development.