Machine Learning (ML) has emerged as a necessary enabler of privacy auditing and, consequently, more robust compliance frameworks for contemporary data spaces. The ubiquity of interconnected models, especially in use cases like the Internet of Things (IoT), cloud computing, and federated learning (FL), has brought forth daunting challenges around data privacy, security, and support for regulatory requirements. This paper provides a panoramic view of cutting-edge research that falls under the paradigm of ML and privacy auditing and includes recent trends in threat monitoring, data integrity verification, automation of regulatory compliance, and privacy-preserving algorithms. Research studies from 2020-2025 have been included to bring the manuscript up to date on the current techno-regulatory environment. The study delves into basic techniques like differential privacy, integration with blockchain technology, and FL to assess their implications on the role of ML to hold data accountable. Following a recent literature stream, the review outlines current limitations and suggested directions for research on scalable, interpretable, and regulation-aware ML-based systems for privacy auditing.

Machine Learning Privacy Auditing Data Compliance Federated Learning Differential Privacy