The capacity for ransomware groups to substantially impair computer systems, data centres, websites, and mobile apps in a number of businesses and professions makes them serious security risks for cybersecurity. Conventional anti-ransomware software developers struggle to counter newly created, complex threats. Therefore, employing modern methods such as classical and neural network-based designs, the construction of creative ransomware cures can be accomplished effectively. On a chosen set of attributes for categorizing ransomware, investigators applied a range of machine learning techniques, including Random Forest (RF), Logistic Regression (LR), SVM, KNN, and decision tree. To evaluate the suggested strategy, we ran each test on a single ransomware sample. For example, ransomware frequently rushes through a variety of document-related activities in order to lock or encrypted the files on a victim's computer. Users' data can't be effectively protected against assaults carried on by hazardous unrecognized ransomware when using signature-based malware detection techniques, due to issues identifying zero-day ransomware