This study investigates the use of machine learning (ML) techniques to improve nuclear intrusion detection. In the context of nuclear warfare, which is constantly evolving, conventional technologies may be unable to detect sophisticated, adaptive assaults. For this purpose, ML techniques including Isolation Forest for unsupervised learning and Convolutional Neural Networks (CNN) for supervised learning are investigated. To evaluate these ML methods, case studies utilizing data from operational nuclear systems are conducted. Both Isolation Forest and CNN are effective intrusion detectors, with CNN outperforming the baseline and Isolation Forest. This investigates the challenges of employing ML for intrusion detection in nuclear systems and demonstrate how CNN may assist by extracting subtle patterns and features from large datasets to improve the accuracy of detection. This research contributes to the improvement of nuclear system security and casts light on how to apply machine learning techniques to intrusion detection.