In this study, a real-time motion control strategy for robotic arms based on deep learning technology is explored to enhance accuracy and stability in complex operational environments. Utilizing a high-performance hardware platform and an advanced robotic arm model, various sensors are employed to monitor the arm’s status in real-time for data collection and preprocessing. The deep neural network, which is the main forecasting instrument, will be trained using this pre-processed data and will therefore be able to predict the correct motion control strategy for the robotic arm. The results of the experimental study demonstrate that the strategy proves itself to be a very effective tool in improving the accuracy and stability of the robotic arm when it performs complicated tasks. The actual outcomes are that the real-time motion control strategy based on deep learning is very efficient when it comes to robotic arm motion control. The paper delivers new concepts and techniques that can be applied to the robot arm with high precision control, which makes it a great theoretical value and it has a high degree of practical application potential.