Machine Unlearning: A Comprehensive Framework for Efficient Data Removal in Deep Learning Systems

The rapid proliferation of machine learning models trained on sensitive data has intensified global privacy concerns and regulatory demands for the “right to be forgotten.” Machine unlearning has emerged as a promising paradigm to remove specific data influences from trained models without complete retraining. This paper presents a conceptual hybrid framework that integrates influence estimation, selective parameter adjustment, and verification mechanisms to achieve efficient and verifiable unlearning in deep learning systems. Rather than providing empirical benchmarks, this work synthesizes theoretical foundations and algorithmic design strategies to establish a unified basis for balancing computational efficiency, model utility, and regulatory compliance. The proposed approach also highlights the ethical and accountability dimensions of unlearning, emphasizing its role in trustworthy and privacy-preserving AI. This framework offers a structured pathway for future experimental validation and real-world deployment of scalable unlearning solutions.