Detecting diseases in fish farms is crucial for ensuring food security. However, spotting infections in fish early on is challenging due to the lack of necessary infrastructure. Detecting salmon fish diseases in aquaculture is particularly important because salmon farming is rapidly growing and contributes significantly to the market. To address this, author used combination of image processing and machine learning techniques to identify diseases caused by various pathogens in infected fish. In the first part, we apply image preprocessing and segmentation techniques to reduce image noise and enhance image details. In the second part, we extract important features from the processed images and use the support vector machine algorithm, a type of machine learning with a kernel function classify the diseases. We conducted experiments using a dataset. By using Gradient Boosting Algorithm, we can able to detect the health of a fish is good or bad. By using water parameters like temperature, pH, oxygen level, chloroform, and any another soluble chemicals. By normalizing the data set entities, we can able to take features selection. We can came across the graph. Then we need to train that data to the algorithm. The accuracy of the algorithm will be 95–97.