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CR-39 solid-state nuclear track detectors are widely used in fusion research for detecting charged particles produced in fusion reactions. However, analyzing increasingly complex and large-scale CR-39 track images to extract meaningful information can be a tedious and time-consuming process, often prone to human errors and bias. To address these challenges, we developed an AI-based classification model capable of differentiating protons, tritons, and helions produced during D-D fusion, using CR-39 track images as input data. The CR-39 track images were processed and used to train a deep learning model. By preprocessing the track images for noise reduction and feature enhancement, we trained the YOLOv8 [1][2] network to distinguish the three particle types with high accuracy. The proposed model achieved a classification accuracy of over 96%, demonstrating its potential for improving automated track analysis in CR-39 detectors. Additionally, the model precisely identifies particle coordinates and counts, enabling comprehensive particle analysis. This study highlights the application of AI in track detection and classification, offering a robust solution for particle identification in CR-39 detector-based experiments.