<p>Deep learning (DL) has been recognized as a transformative advancement in artificial neural network research, leading to breakthroughs across a wide range of scientific and engineering domains. Despite this progress, the effective modeling and prediction of high-dimensional dynamical systems with spatiotemporal complexity continue to pose significant challenges, particularly in physical system modeling, where traditional methods often depend on extensive parameterization and deep domain expertise. This review paper was undertaken to explore the potential of DL in addressing these challenges by examining its role in spatiotemporal prediction tasks. A comprehensive synthesis of the evolution, architecture, and application of DL models was carried out, with a focus on their strengths and limitations in capturing spatiotemporal dependencies. Findings from recent literature were analyzed, revealing that DL methods have demonstrated substantial capability in extracting spatial and temporal patterns from complex datasets. Applications in climate modeling, traffic forecasting, air quality prediction, and disease outbreak detection have shown that DL models can outperform conventional methods in both accuracy and adaptability. Emerging strategies such as physics-informed networks and spatiotemporal attention mechanisms have been identified as promising directions to improve model interpretability, generalizability, and physical consistency. It is concluded that DL techniques offer significant advantages in spatiotemporal modeling by eliminating the need for manual feature engineering and enabling scalable, data-driven analysis. However, practical deployment in real-world systems remains in early stages and requires further development in areas such as uncertainty quantification, scientific integration, and model complexity management. This review aims to serve as a reference for researchers and practitioners seeking a foundational and forward-looking understanding of DL’s potential in physical system modeling.</p>