This paper provides a comprehensive review of interpretation techniques in explainable artificial intelligence (XAI), emphasizing their foundational principles, historical context, and evolving landscape. The authors categorize these techniques into model-based, representation-based, post hoc, and hybrid methods, detailing their origins, principles, and applications. They highlight the importance of transparency and explainability in AI models for human-AI collaboration and regulatory compliance. The review also analyzes publication trends over time, noting a shift towards data-driven approaches and the integration of advanced computational methods. Key techniques such as rule-based models, Bayesian rule lists, decision trees, random forests, linear and logistic regression, global sensitivity analysis, Bayesian networks, saliency maps, activation maximization, attention mechanisms, LIME, SHAP, DeepLIFT, and Grad-CAM are discussed in depth. The paper concludes by discussing the challenges and limitations of these techniques and their suitability for different data types, including images, text, and tabular data. The review aims to enhance AI model explainability and promote ethical and practical use of interpretation insights in real-world scenarios.This paper provides a comprehensive review of interpretation techniques in explainable artificial intelligence (XAI), emphasizing their foundational principles, historical context, and evolving landscape. The authors categorize these techniques into model-based, representation-based, post hoc, and hybrid methods, detailing their origins, principles, and applications. They highlight the importance of transparency and explainability in AI models for human-AI collaboration and regulatory compliance. The review also analyzes publication trends over time, noting a shift towards data-driven approaches and the integration of advanced computational methods. Key techniques such as rule-based models, Bayesian rule lists, decision trees, random forests, linear and logistic regression, global sensitivity analysis, Bayesian networks, saliency maps, activation maximization, attention mechanisms, LIME, SHAP, DeepLIFT, and Grad-CAM are discussed in depth. The paper concludes by discussing the challenges and limitations of these techniques and their suitability for different data types, including images, text, and tabular data. The review aims to enhance AI model explainability and promote ethical and practical use of interpretation insights in real-world scenarios.