Ransomware Detection Using Machine Learning and Explainable AI research introduces a novel approach to addressing the significant threat posed by ransomware attacks, which traditional signature-based detection methods often fail to detect due to the evolving nature of ransomware variants. To counter this, the proposed system leverages advanced machine learning (ML) algorithms, including support vector machines (SVMs), random forests, and deep learning models, to analyze file behavior and network traffic patterns indicative of ransomware activity. The system incorporates feature extraction techniques to identify critical characteristics such as file encryption rates, unusual file access patterns, and network communication anomalies. Additionally, explainable artificial intelligence (AI) methods are integrated to enhance transparency and interpretability in the ransomware detection process. Techniques such as feature importance ranking, Local Interpretable Model-agnostic Explanations (LIME), and random forest visualization are employed to elucidate the reasoning behind the model’s predictions and highlight key indicators of ransomware activity. Through continuous learning and adaptation to new ransomware threats, the proposed approach improves detection accuracy and enables security analysts to understand the underlying causes of ransomware alerts, facilitating more effective response strategies. Experimental evaluations using real-world ransomware datasets demonstrate the system’s effectiveness and robustness in accurately detecting ransomware attacks while providing interpretable insights for cybersecurity professionals. Among the various algorithms evaluated, the random forest algorithm demonstrated superior performance, achieving an accuracy of 99.99%. These findings underscore the importance of integrating machine learning techniques and explainable artificial intelligence techniques into ransomware defense mechanisms to enhance threat detection and mitigation capabilities.