The Black Box in AI refers to the lack of transparency and interpretability in intelligent systems. AI algorithms learn from data, making it difficult to trace decision-making. This poses implications for critical applications and raises ethical concerns. Approaches like Explainable AI, model transparency, regulatory frameworks, and open-source initiatives aim to address the Black Box problem. These efforts provide human-understandable explanations, improve model interpretability, ensure compliance, and foster transparency. Unveiling the secrets within the Black Box is vital for building trust and deploying AI responsibly.