Enterprise organizations increasingly rely on large-scale, heterogeneous data generated from transactional systems, cloud platforms, Internet of Things (IoT) devices, business applications, and external data sources to support operational processes, business intelligence, and artificial intelligence initiatives. However, maintaining the accuracy, consistency, completeness, and reliability of enterprise data remains a significant challenge due to the growing complexity, volume, and velocity of modern information ecosystems. Conventional rule-based data validation techniques often struggle to detect evolving anomalies, hidden data inconsistencies, and complex quality issues in real time, resulting in reduced analytical accuracy and increased operational risk. This paper proposes A Hybrid AI and Statistical Framework for Enterprise Data Validation and Anomaly Detection, which integrates statistical validation methods with artificial intelligence, machine learning, predictive analytics, metadata-driven governance, and automated anomaly detection to provide a comprehensive enterprise data validation solution. The proposed framework combines statistical techniques such as distribution analysis, correlation analysis, outlier detection, hypothesis testing, and confidence interval estimation with machine learning algorithms capable of identifying complex anomaly patterns, predicting potential data quality degradation, and continuously adapting to evolving enterprise environments. A centralized metadata repository, governance policies, and continuous monitoring mechanisms enhance data transparency, lineage, compliance, and automated decision-making while supporting scalable deployment across cloud-native and hybrid enterprise architectures. Intelligent validation workflows automate data profiling, quality assessment, anomaly classification, remediation recommendations, and governance reporting, significantly reducing manual intervention and improving operational efficiency. By integrating explainable artificial intelligence, adaptive learning, and predictive quality analytics, the proposed framework enhances enterprise data reliability, strengthens regulatory compliance, improves business intelligence, and provides a robust foundation for intelligent data management and sustainable digital transformation across modern enterprise information systems.