In India, increased internet usage and technological developments have led to the emergence of cybercrimes like identity theft, ransomware attacks, internet fraud, phishing and scams, and breaches of confidentiality etc. The Information Technology Act 2000, IT Amendment Act, 2008, and other allied laws, rules, and regulations exist in India to deal with cyber offences however, the existing legal framework seems inadequate unless machine learning is used to detect and prevent cybercrimes. Also, the existing judicial system has severe issues, such as excessive trial wait times, a lack of tech-savvy judges, and poor digital proof utilization. To overcome the issues associated with cybercrimes, the role of Machine Learning (ML) will be decisive. The ML use will help to identify patterns and predict security vulnerabilities from large data sets. Having this background in mind, this research aims to examine India’s cybersecurity using machine learning-driven case analysis. The classification algorithm and Natural Language Processing (NLP) approach may assist police in identifying legal patterns, outcomes, and recurring difficulties. Future research emphasizing Machine Learning will improve prediction models and datasets for better outcomes, particularly in detecting cybercrimes and dealing with cybercrimes-related pendency of cases in Indian courts. The combination of computer/data scientists and law enforcement agencies would bring drastic changes in modus operandi to deal with cybercrimes. Along with changes in the existing legal framework, the application of Machine Learning offers hope and solutions.