Using machine learning (ML) techniques in safe software development has become an important part of making security stronger against new cyber dangers. One important area of focus is vulnerability identification. ML algorithms look at code trends and past data to find possible security holes before attackers can use them. This lowers the risk of attacks before they happen. At the same time, anomaly detection techniques use the power of machine learning to find strange behavior in software systems. These techniques act as early warning systems for possible security breaches or bad behavior. Machine learning is also a key part of improving danger data and finding malware. ML algorithms can find patterns and traits that are typical of harmful code by looking at huge files of known malware samples. This helps find new threats quickly and stop them before they cause damage. ML-based models are also good at improving access control by looking at user behavior patterns and access records. This makes security methods stronger and stops people from getting in without permission. To make sure that ML-driven security solutions are reliable and effective, things like protecting data privacy, making models easier to understand, and stopping threats from other people must be carefully thought out. Regardless, companies can constantly find and fix security risks by using data-driven insights. This makes their software systems more resilient and strong in the face of changing cyber dangers.