Aquaculture is a fundamental industry that largely supports the world-wide food chain. Yet, providing the best quality water is never an easy feat because of variable environmental conditions and pollution. Herein, a system for an AI-based aquaculture control system with embedded 7-in-1 Total Dissolved Solids (TDS) Meter, real-time sensor monitoring, and machine learning algorithms to help improve water quality measurement and forecast fish growth has been proposed. Through our analysis of salinity, temperature, nitrate, ammonia, and pH, our model groups fish species into three groups: cold-water predatory species, adaptable species, and sensitive species that need to be kept in controlled environments. Classification and prediction of fish growth rates are performed using logistic regression based on environmental variables. The model illustrates an accuracy of 97%, clearly differentiating species from water parameters and accurately predicting the best conditions for fish growth. With IoT sensors, AI analytics, and human inputs, aquaculture experts can optimize farming techniques, minimize mortality, and increase productivity. This study identifies the future of smart aquaculture ecosystems in promoting sustainable and efficient fish farming with predictive analytics and real-time monitoring.