With the constantly evolving regulatory landscape, organizations face high financial, legal, and reputational risks. To cope with these risks effectively, a holistic approach needs to be implemented, which includes periodic audits, targeted employee training, and cutting-edge regulatory technology. In this paper, we present a framework that employs machine learning techniques to predict regulatory violation rates. By using advanced algorithms and data analytics, our model not only identifies potential compliance breaches but also facilitates proactive decision-making and risk prevention. The use of machine learning enhances the accuracy and efficiency of compliance predictions, thereby enabling organizations to be a step ahead of regulatory challenges. We conduct a detailed analysis of real-world data from different sectors, employing a range of machine-learning algorithms to develop a predictive model. The results of the model demonstrate the efficacy of our approach in accurately forecasting regulatory violations. Additionally, we explore the effects of periodic audits, employee training programs, and regulatory technology to enhance overall compliance. This paper contributes valuable insights to the field of regulatory compliance and machine learning applications. The findings from the research provide a path for companies to proactively prevent financial losses, legal complications, and reputational damage. By embracing this holistic approach, organizations can create a culture of compliance, ensuring sustainable growth and resilience in the face of regulatory challenges. It also emphasizes the importance of continuous improvement, suggesting that a dynamic approach to compliance, informed by real-time data and machine learning insights, is pivotal in maintaining robust regulatory adherence and safeguarding organizational integrity.