Unveiling the Black Box: Practical Deep Learning and Explainable AI" offers a comprehensive overview of Explainable AI (XAI) techniques and their significance in ensuring transparency and trust in complex AI models. With AI applications spanning healthcare, finance, and autonomous systems, the opacity of deep learning models often raises ethical, legal, and reliability concerns. This guide explores foundational AI model structures, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their architecture, functionality, and real-world applications. To enhance interpretability, the text introduces leading XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP), which enable users to understand model predictions. Advanced techniques, including Transfer Learning and Attention Mechanisms, are discussed to illustrate their impact on neural network adaptability and performance. The challenges of achieving interpretable AI, such as managing bias, balancing accuracy, and ensuring privacy, are also addressed.