Ransomware attacks have become a significant cybersecurity threat, causing severe financial and operational damage to enterprises worldwide. Traditional security measures often fail to detect and mitigate these threats before they inflict harm. This paper explores the application of machine learning (ML) techniques for the early detection of ransomware attacks in enterprise networks. By analyzing network traffic patterns, system behaviors, and anomaly detection methods, ML models can identify suspicious activities indicative of ransomware execution. The study evaluates various supervised and unsupervised learning algorithms, including decision trees, support vector machines (SVM), deep learning, and clustering techniques. Experimental results demonstrate that ML-based approaches can enhance the accuracy and efficiency of ransomware detection, minimizing response times and reducing potential losses. The findings suggest that integrating machine learning into cybersecurity frameworks can significantly improve an organization’s resilience against ransomware threats.