As global aquaculture expands to meet rising food security demands, managing fish health? especially gut health?has become vital for industry sustainability and financial success. For fish, the gastrointestinal tract acts as the control centre for nutrient absorption, immune regulation, and generally boosts performance. However, traditional methods of monitoring gut health, such as histological analysis, necropsy, or visual inspection of mortality events, are inherently reactive, invasive, and often not timely. This paper examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in shifting intestine health management from a reactive approach to a proactive, real-time, precisionbased method. Advanced convolutional neural networks (CNNs) are proposed for the automatic, real-time assessment of faecal waste characteristics?such as buoyancy, color, and viscosity?immediately in the water column, serving as instant indicators of digestive function and dysbiosis. Additionally, the paper highlights how AI can analyse complex, highthroughput sequencing data from the fish intestine microbiome. By employing deep learning algorithms to interpret metagenomic data, researchers can now detect early microbial biomarkers of strain or pathogen intrusion well before clinical symptoms emerge.