Enterprises operating at scale increasingly depend on accurate, consistent, and trustworthy data to support analytics, regulatory reporting, operational execution, and strategic decision making, yet traditional rule-based data quality programs have struggled to keep pace with rising data volumes, heterogeneous data sources, and rapidly changing business conditions. This study addresses the persistent gap between static data validation techniques and the dynamic error patterns that emerge within modern enterprise platforms by developing and evaluating machine learning driven frameworks designed to identify, classify, and prioritize data quality issues with higher precision and adaptability. Using a mixed methodological approach that integrates architectural analysis, quantitative experimentation, and scenario-oriented evaluation, the research examines how supervised learning models, probabilistic classifiers, and feature engineered validation pipelines can outperform conventional rule sets in detecting anomalies, missing values, inconsistent records, and entity level conflicts across complex datasets. Findings demonstrate that machine learning models achieve substantial gains in accuracy, recall, and operational robustness, while also improving the interpretability of validation decisions when embedded within structured enterprise workflows. The study presents innovative validation strategies that combine predictive modeling with governance-oriented feedback loops, enabling organizations to respond to evolving data behaviors and reduce downstream operational risks. By contributing reference architecture, empirical benchmarks, and applied insights, the research advances both academic understanding and industry practice in enterprise data quality management. The results indicate that machine learning enabled validation provides a scalable and future ready foundation for enterprises seeking to strengthen trust in data assets and enhance the resilience of data dependent operations.