Urban traffic flow management and delay reduction in transportation networks represent major challenges in metropolitan areas. Considering the complexity and dynamic nature of traffic flows, employing advanced machine learning and deep learning techniques offers an effective approach to enhancing the efficiency of urban transportation systems. In this research, real-world traffic data were utilized and preprocessed through handling missing values, normalization, and origin–destination matrix construction. Subsequently, a set of machine learning models, including Random Forest and XGBoost, along with deep learning architectures such as LSTM, Attention-based LSTM (A-LSTM), and Convolutional LSTM (ConvLSTM), were developed and evaluated. The results revealed that the ConvLSTM model equipped with an attention mechanism achieved the highest predictive performance, with a coefficient of determination R²=0.9331. The A-LSTM (R²=0.9290) and baseline LSTM (R²=0.8929) models ranked next, whereas tree-based models exhibited inferior accuracy due to their inability to capture temporal dependencies. The principal contribution of this thesis lies in the design and implementation of a hybrid ConvLSTM framework augmented with an attention mechanism, enabling simultaneous spatiotemporal feature extraction and substantially improving predictive accuracy. The findings provide a robust foundation for the development of intelligent traffic control systems and evidence-based policymaking in the domain of urban transportation management.