Given the increasing prevalence and sophistication of ransomware attacks, there is an escalating need for dynamic and effective detection and mitigation strategies. Traditional mark-based methodologies often exhibit deficiencies in identifying new and emerging variants of ransomware. This research examines the use of machine learning techniques for ransomware detection, aiming to enhance the accuracy and adaptability of detection tools. It offers a comprehensive analysis of several machine learning techniques and algorithms, evaluating their effectiveness in detecting ransomware trends. The results provide critical insights into the evolution of cybersecurity strategies that are more robust and proactive in addressing the evolving environment of ransomware threats.