Deep learning (DL) algorithms attain exceptional outcomes in several fields but face two principal challenges: generalization and explainability. Generalization and explainability are fundamental principles for effective and reliable DL models. Generalization denotes a model’s capacity to excel on unfamiliar data, whereas inadequate generalization, sometimes resulting from overfitting, might impede the model’s practical use. Generalization and explainability are crucial for implementing deep learning models in practical applications where the test distribution may differ from the training distribution. Explainability pertains to the necessity for transparent and interpretable models, which is essential for trust and accountability, particularly in sensitive domains such as healthcare and finance. The opacity of decision-making processes in deep learning models constrains their use in critical applications. This chapter presents the issues of generalization and explainability in deep learning, emphasizing the key factors that affect them, the challenges involved, techniques to enhance both, and the current progress in the field.