Machine unlearning is a technique that removes specific data influences from trained models without the need for extensive retraining. However, it faces several key challenges, including the lack of explanation, privacy concerns during the unlearning process, and the high demand for time and computational resources. This paper presents a novel unlearning approach to tackle above challenges by employing Layer-wise Relevance Analysis and Neuronal Path Perturbation. Our method balances machine unlearning performance and model utility by identifying and perturbing highly relevant neurons, thus achieving effective unlearning. Using unseen data that has not been presented in the original training set, our method achieves zero-shot unlearning, which allows for the removal of specific class knowledge without accessing the original training data during the unlearning process. This approach ensures robust privacy protection. Experimental results demonstrate that our approach effectively removes targeted data from the target unlearning model while maintaining the model’s utility, offering a practical solution for privacy-preserving machine learning.