Traffic prediction is an essential component of Intelligent Transportation Systems (ITS), enabling improved traffic management, congestion reduction, and enhanced decision-making through accurate short-term forecasts. This paper provides a comprehensive review of advanced methodologies in traffic prediction, with a particular emphasis on hybrid models that integrate spatial and temporal learning techniques, such as Graph Neural Networks (GNNs) combined with Recurrent Neural Networks (RNNs). These models effectively capture the complex spatial-temporal dependencies inherent in traffic data. We thoroughly examine key challenges in the field, including data quality issues, class imbalance, and the integration of heterogeneous data sources, which hinder the effectiveness and scalability of current prediction methods. The review also explores innovative solutions, such as multi-task learning frameworks and advanced data augmentation techniques, which address these challenges and enhance model performance. Furthermore, we highlight the growing demand for scalable, real-time predictive models capable of processing large-scale, dynamic traffic data streams, particularly in the context of computational complexity and latency constraints. By synthesizing current advancements and identifying open research directions, this review aims to guide future efforts in developing robust and efficient traffic prediction systems.