Differential equations have become increasingly integral to the development of neural network architectures, providing a continuous-time framework that enhances both the theoretical understanding and practical application of deep learning models. This review explores the mathematical foundations of integrating ordinary and partial differential equations (ODEs and PDEs) into neural networks, with a focus on Neural Ordinary Differential Equations (Neural ODEs) and Physics-Informed Neural Networks (PINNs). We discuss the theoretical insights gained from this integration, including stability, convergence, and generalization, and examine key applications in time-series forecasting, generative modeling, control systems, and biological systems. Despite their promise, these models face challenges related to computational complexity, stability, scalability, and interpretability. Future research directions include the development of specialized numerical solvers, enhancing model robustness, scaling to high-dimensional problems, and improving interpretability. This paper provides a comprehensive overview of the current state and future prospects of differential equation-based neural networks, highlighting their potential to advance both theoretical and applied machine learning.