The rapid growth of deep neural networks (DNNs) in both model size and deployment scale has placed renewed emphasis on optimization techniques that balance convergence speed, numerical stability, computational efficiency, and resource utilization, particularly as training workloads increasingly span heterogeneous hardware platforms and distributed computing environments. This article presents a systematic analysis of optimization methods for large-scale deep learning, encompassing stochastic first-order approaches such as momentum-based gradient descent, adaptive optimizers that adjust learning rates based on gradient statistics, normalization strategies that stabilize internal representations and smooth optimization landscapes, curvature-aware methods that incorporate second-order information, and system-level techniques including large-batch, mixed-precision, and distributed training. Drawing on publicly available empirical evidence and widely cited foundational studies, we examine how optimization choices shape training dynamics, scalability characteristics, convergence behavior, and final model performance across diverse deep learning workloads. Using representative figures from prior work—including Batch Normalization training dynamics and adaptive optimization formulations—we synthesize practical guidance for selecting and tuning optimization strategies at scale while identifying persistent challenges related to generalization, communication efficiency, and the alignment of optimization algorithms with modern hardware and system architectures.