The increasing demand for seafood across the world is mostly met by aquaculture. However, there are difficulties with fish growth and health monitoring. The development of artificial intelligence (AI) methods presents viable ways to guarantee sustainable aquaculture and improve fish farming methods. AI encompasses the utilization of computer vision with machine learning, which has demonstrated enormous promise in analyzing the huge amounts of data gathered from fish farms. Fish farmers may obtain important insights into fish growth trends, eating habits, and environmental factors, impacting fish health by utilizing AI algorithms. It helps to identify and forecast physiological abnormalities and illnesses, and through their stress biomarkers, these algorithms can predict preventative measures through various models to minimize health problems and lower costs. One of the main uses of AI in aquaculture is the creation of intelligent monitoring platforms. These systems continuously gather data in real-time on temperature, oxygen levels, fish behavioral activity, and water quality using a variety of sensors, cameras, and data analytics tools. With the use of AI algorithms, these data are analyzed to find various deviations from ideal circumstances and promptly notify farmers, enabling them to make the necessary adjustments like changing water parameters, feeding schedules, or treating crops as needed. AI-based models can also help save waste and improve feed management. By analyzing historical data on fish development and feed intake, machine learning algorithms can identify the most effective feed composition and feeding schedules, resulting in higher growth rates and less environmental impact. Identifying and controlling illness is a key component of artificial intelligence in fish farming. Artificial intelligence (AI) systems can detect early indicators of illnesses, such as parasite infection or anomalies in fish appearance and changes in behavior, using a CMOS camera through pattern recognition. This improves fish welfare by enabling early disease detection and focused treatment, which could lower the need for overuse of antibiotics and other chemicals. In conclusion, the sustainability of aquaculture is greatly enhanced by using AI techniques in fish development and health status monitoring. Fish farmers can improve their methods, increase production, lessen their impact on the environment, and guarantee the well-being of their farmed fish by utilizing AI’s strengths in data analysis, pattern recognition, and predictive modeling. However, the maximum utilization of AI in a sustainable aquaculture sector needs further research, data exchange, and cooperation between scientists, industry players, and legislators.