In this article we describe the development of machine learning models to assist the CLAS12 tracking algorithm by identifying the best track candidates from combinatorial track candidates from the hits in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptron (MLP) and Extremely Randomized Trees (ERT). The final implementation was based on an MLP network and provided an accuracy $>99\%$. The implementation of AI assisted tracking into the CLAS12 reconstruction workflow and provided a 6 times code speedup.

Using Artificial Intelligence for Particle Track Identification in CLAS12 Detector