Fish farming can help meet the ever-increasing demand for seafood and offer nutritious protein to people in underdeveloped countries, while additionally minimizing pressure on wild fish. The global population is supported economically by the fishing industry and related sectors for 10–12% of the total. Presently, cutting-edge technologies such as AI, Machine Learning, Automated Planning, Data Analytics, and IoT are being used in fish farming to increase production, efficiency, and sustainability. In this research, we proposed an IoT-based improved technique for fish feeding and monitoring that will enhance the efficiency of fish farming and increase production while avoiding threats to the environment. This proposed approach uses a microcontrollerESP32 that gathers data from temperature, turbidity, pH, and ultrasonic sensors, alongside a Wi-Fi webcam detecting fish movement. It will be linked to the Blynk app, which gathers and sends this data, informing users about water conditions. Another remarkable feature is the ability for customers to remotely initiate water-changing procedures and customize feeding parameters using their mobile devices. This method aims to reveal an unprecedented integration of essential sensors encompassing fish rearing, fish movement tracking, remotely manipulating wastewater via a smartphone, supervising the feeding system, and comprehensive water quality monitoring, a unique combination that distinguishes it from existing research. Based on the test results, the servo motor for feeding and changing water gave successful results every time, the success rate of every sensor was around 87% – 97% with an error rate of not more than 7%. Future endeavors involve extending this research to encompass varying pond sizes, incorporating tailored sensors optimized for pond conditions, thus rendering them apt for fish culture.