Human–Computer Interaction (HCI) systems have become an increasingly important part of our daily lives with the continuous development of technology. Among them, gesture control stands out as a promising and intuitive means of communication between users and machines. This paper discusses the importance of hand gesture control systems and enunciates the need for research in this area. Since traditional input methods have limitations in terms of naturalness and user engagement, the integration of deep learning techniques becomes important. This research focuses on exploiting two powerful deep learning architectures: 3D Convolutional Neural Networks (3DCNN) and Recurrent Neural Networks (RNN). 3DCNN facilitates the extraction of spatiotemporal features from gesture data, which enables a comprehensive understanding of dynamic hand movements in three-dimensional space. At the same time, RNN helps capture the sequential dependencies inherent in gestures, improving the model and its ability to detect and interpret complex hand movements over time. Deep learning techniques are used to map specific hand gestures to corresponding software controls. By integrating these techniques, our research aims to overcome current challenges in gesture recognition, such as robustness to variations in hand position and movements. This paper discusses the design, execution and evaluation of a hand gesture control system using 3DCNN, with optimization possible using RNN techniques. It provides an analysis of its effectiveness and efficiency when combining hand gestures and software controls. The proposed system has the potential to find applications in various fields such as virtual reality, gaming, and human–robot interaction, paving the way for next-generation intuitive and immersive user interfaces.