Rapid digital infrastructure growth has advanced several sectors in today's interconnected society. Digital change increases vulnerabilities, especially in cybersecurity. Ransomware is a unique cyber danger because thieves’ profit from it. Ransomware targets victims by encrypting their data and demanding money to decrypt it. Such attacks can cause significant financial losses, operational downtime, and customer and stakeholder distrust. Ransomware has progressed from locker malware that locked people out to sophisticated forms that encrypt data and spread across networks. Signature-based antivirus and malware scanners are less effective due to this change. These technologies require malware knowledge to detect and block it, which is essential for ransomware variations that can change signatures or conceal. Cybersecurity protections must change as fast as attacks. Cybersecurity paradigms evolve with ML and DL-based detection techniques. These systems can evaluate patterns, learn from data, and make choices without human interaction, identifying new and changing dangers based on their actions. ML techniques and DL networks create a hybrid model to detect and classify ransomware. The accuracy, precision, recall, and F1-score of different models are evaluated to determine their practical applicability and limitations in real-world circumstances. The integration of these models into a user-friendly application allows real-time detection, meeting the demand for robust and adaptable security solutions that can predict new threats and neutralize ransomware before it does irreparable damage. Our research shows that advanced predictive solutions to detect anomalous activity, robust encryption to secure data, and constant network behavior monitoring to prevent ransomware lateral migration are essential. This work improves cybersecurity by using ML's predictive capacity and DL's pattern recognition ability to defend against ransomware's ever-changing terrain.

Index Terms- Deep Learning, Machine Learning, Cyber Security