The rapid proliferation of digital technologies and the explosion of big data have fundamentally altered the landscape of individual privacy, creating a regulatory gap where the pace of legal adaptation lags significantly behind technological innovation. This study addresses the urgent need for a more adaptive and effective regulatory framework by exploring the determinants of digital privacy enforcement outcomes across diverse global jurisdictions. Using a robust mixed-methods approach, we analyzed 200 enforcement cases from 15 jurisdictions, 120 regulatory documents, and 45 in-depth expert interviews. We employ a Logistic Regression Model to identify the factors most significantly associated with the imposition of high penalties, thereby providing a predictive framework for regulatory effectiveness. Our findings reveal that the severity of the breach, the size of the company, and the frequency of repeat violations are the most critical drivers of penalty levels. Furthermore, comparative analysis highlights that jurisdictions with adaptive frameworks and a strong commitment to enforcement, such as those adhering to GDPR principles, achieve the highest compliance rates. This research offers a structured guide for policymakers and legal practitioners, providing empirical evidence and best practices to inform the development of scientifically rigorous, equitable, and forward-looking digital privacy regulations that can effectively govern emerging technologies like Artificial Intelligence and cross-border data flows.