Particle tracking is an essential part of any high-energy physics experiment. Well-known tracking algorithms based on the Kalman filter are not scaling well with the amounts of data being produced in modern experiments. In our work we present a particle tracking approach based on deep neural networks for the BM@N experiment and future SPD experiment. We have already applied similar approaches for BM@N RUN 6 and BES-III Monte-Carlo simulation data. This work is the next step in our ongoing study of tracking with the help of machine learning. Revised algorithms - combination of Recurrent Neural Network (RNN) and Graph Neural Network (GNN) for the BM@N RUN 7 Monte-Carlo simulation data, and GNN for the preliminary SPD Monte-Carlo simulation data are presented. Results of the track efficiency and processing speed for both experiments are demonstrated.