Generalization remains a central objective in machine learning and deep learning, as the reliability of predictive systems depends not only on their capacity to fit training data but also on their ability to perform consistently under unseen conditions. For decades, the bias--variance trade-off has served as a foundational principle in statistical learning theory, offering a conceptual framework to balance model complexity and predictive stability. However, the rapid evolution of deep neural networks, characterized by large parameter spaces, non-convex optimization, and data-intensive training pipelines, has exposed empirical behaviors that challenge classical assumptions. In particular, modern architectures trained on large-scale datasets such as ImageNet, CIFAR-10, and OpenML benchmarks often demonstrate improved test performance despite substantial model overparameterization, thereby contradicting the conventional expectation of inevitable overfitting. This review critically examines recent theoretical and experimental developments that attempt to reconcile these observations with emerging perspectives on generalization. The analysis synthesizes insights from contemporary learning paradigms, including stochastic gradient-based optimization, implicit regularization effects, and scaling dynamics observed in transformer and convolutional neural network architectures. Emphasis is placed on understanding phenomena such as double descent behavior, robustness under distributional variation, and the role of data representation in shaping model generalization boundaries. By consolidating findings from diverse empirical studies and theoretical models, this work identifies structural limitations in traditional bias--variance interpretations and highlights promising directions for advancing reliable learning systems. The contribution of this review lies in providing a coherent and technically grounded synthesis of modern generalization theories, offering practical guidance for the design and evaluation of scalable deep learning models in complex real-world environments.

Bias--Variance Trade-off, Deep Learning Generalization, Overparameterization, Double Descent Phenomenon, Implicit Regularization, Model Complexity, Statistical Learning Theory