Real-time hand gesture recognition system with Arduino-based tools has developed. this work presents an artificial intelligence (AI) powered innovative method for real-time recognition of hand gestures to operate robots. This project’s fundamental concept is a machine-learning model taught on a customized dataset of hand gestures. Hand annotations made in great care in this dataset ensure accuracy. Using data augmentation techniques improved generalizing ability and model performance. The model also makes use of transfer learning, in which case the basis is a ResNet backbone to efficiently learn from the dataset. In addition, a custom robot built on Arduino and Raspberry Pi accompanied the development of the artificial intelligence model. This robot features a camera that records hand gestures, which are then provided to the machine learning model for instantaneous analysis. The hardware of the robot was precisely tuned to provide exact data acquisition and simple operation. The resulting system allows the robot to instantly recognize hand gestures, so generating many opportunities for use from smart home technology to industrial automation. This project offers the ability to create original solutions for practical problems using robotics, computer vision, as well as artificial intelligence. It also significantly improves robot adaptability and performance. It advances a better human-computer interface by properly applying creative AI and computer vision technologies.