Article’s

Abstract—The paper presents a smart and compact aquatic system that combines IoT sensors, machine learning, and image processing to automate and optimize aquarium maintenance. The system continuously monitors key water parameters such as turbidity, temperature, conductivity, pH and water level, using sensors interfaced with an STM32 microcontroller. A machine learning model analyzes this data to predict water quality in real time, while a CNN-based image processing module detects fish breed such as Bala Shark, Bristenose Pieco ,Clown Loach, Freshwater Angelfish, Neon Tetra, Silver Dollar and Swordtail and fish diseases from captured images. Additionally, the system features automated fish feeding and water level monitoring for improved care and efficiency. Designed for ease of use, the portable setup is ideal for homes, educational institutions and research environments, offering a realiable and intelligent solution for fish keeping with manual effort. Index Terms—Sustainable Aquarium, IoT, Water Quality Prediction, Solar Panel Fish Breed Detection, Fish Disease Detection, Machine Learning, CNN, STM32,Automation.