Hand gesture recognition has the potential to revolutionize the way the presenter interact with computers captivating field of hand gesture recognition highlighting its significance, applications, challenges, and recent advancements. The main concept behind hand gesture identification and recognition is to translate hand movements into commands that can be understood by computers and other devices. This methodology inscriptions a milestone in human–computer interaction (HCI) making technology more accessible and user-friendly. The main of the proposed system is support the presenter to communicate with the slides using hand gestures. The proposed method provides an algorithm for identifying hand movements and perform several functionalities of power point presentation. Its impact spans across industries transforming interactions with systems in areas such as virtual reality, gaming, healthcare, and vehicle interfaces. In this research, the artificial neural network is explored for hand gesture detection, sensor technologies, and machine learning approaches employed in this field. Additionally, the proposed method is examined with different application models and showcase the versatility of this technology and its potential for improving productivity, accessibility and user engagement. However, there are challenges associated with hand gesture identification that include adaptability to conditions variations, in gestures performed by individuals and standardization efforts. The proposed hand gesture classification method developed to solve the challenges and explore research aimed at finding effective solutions.