As Artificial Intelligence systems become more integrated into everyday applications, concerns about data privacy and compliance with the “Right to Be Forgotten” laws have grown. Machine Learning models are increasingly vulnerable to privacy attacks such as membership inference attacks and model inversion attacks, which can reveal sensitive training data. Traditional approaches often require retraining the model from scratch, a costly and time-consuming process. This has led to an increased focus on Machine Unlearning, a process that selectively removes the influences of specific data points from an already-trained model without retraining. However, a reliable and explainable verification method to ensure that unlearning occurred remains to be explored. This research introduces a novel verification method leveraging local explainability to ensure the effectiveness and transparency of unlearning operations. By analyzing feature importance before and after unlearning, the framework provides interpretable evidence of behavioral change. Experiments on the Breast Cancer dataset show that Data Obfuscation and Data Pruning achieve up to 89% accuracy while reducing membership inference risks. This approach supports transparent and accountable AI practices aligned with GDPR, CCPA, and CPPA privacy regulations.