This review provides a comprehensive overview of interpretation techniques in machine learning (ML) and their applications, emphasizing the importance of transparency and explainability in AI systems. The authors categorize interpretation techniques into model-based, representation-based, post hoc, and hybrid methods, analyzing their strengths, limitations, and applications across various domains such as images, text, and tabular data. The review also examines publication trends over time, highlighting a growing preference for data-driven approaches in AI interpretability. It discusses the historical development of AI, from symbolic manipulation to deep learning networks, and explores the evolution of interpretability techniques in the context of explainable AI (XAI). The review emphasizes the need for transparency in AI decision-making to ensure regulatory compliance, user trust, and ethical use of AI. It also addresses the challenges and opportunities in implementing interpretation techniques, including the importance of model robustness, bias detection, and the compatibility of techniques with different data types. The authors highlight the contributions of pioneering researchers and the significance of understanding the origins of interpretation techniques to enhance AI model explainability. The review concludes with a discussion of future research directions and the importance of integrating interpretability into AI systems to promote responsible and ethical AI implementation.This review provides a comprehensive overview of interpretation techniques in machine learning (ML) and their applications, emphasizing the importance of transparency and explainability in AI systems. The authors categorize interpretation techniques into model-based, representation-based, post hoc, and hybrid methods, analyzing their strengths, limitations, and applications across various domains such as images, text, and tabular data. The review also examines publication trends over time, highlighting a growing preference for data-driven approaches in AI interpretability. It discusses the historical development of AI, from symbolic manipulation to deep learning networks, and explores the evolution of interpretability techniques in the context of explainable AI (XAI). The review emphasizes the need for transparency in AI decision-making to ensure regulatory compliance, user trust, and ethical use of AI. It also addresses the challenges and opportunities in implementing interpretation techniques, including the importance of model robustness, bias detection, and the compatibility of techniques with different data types. The authors highlight the contributions of pioneering researchers and the significance of understanding the origins of interpretation techniques to enhance AI model explainability. The review concludes with a discussion of future research directions and the importance of integrating interpretability into AI systems to promote responsible and ethical AI implementation.