Technological advancements in computational power and algorithm design have enabled artificial intelligence to become a transformative force in microbiome research. This paper presents a concise overview of recent applications of this computational paradigm in human and animal health, with a particular emphasis on aquaculture. International projects focused on the intestinal microbiome have allowed human research to consistently dominate in terms of application cases, offering insights into various pathological conditions. In contrast, animal research has leveraged artificial intelligence in microbiome analysis to promote sustainable productivity, addressing environmental and public health concerns linked to livestock husbandry. In aquaculture, on the other hand, artificial intelligence has mainly supported management practices, improving rearing conditions and feeding strategies. When considering microbiome manipulation, however, fish farms have often relied on traditional methods, without harnessing the immense potential of artificial intelligence, whose recent applications include biomonitoring and modeling interactions between microbial communities and environmental factors in farming systems. Given the paradigm shift currently underway in both human health and animal husbandry, we advocate for a transition in the aquaculture industry toward smart farming, whose interconnected infrastructure will allow to fully leverage artificial intelligence to seamlessly integrate both biological measurements and rearing parameters.

To reduce dependence on oceanic resources, poultry-based ingredients and fortified feeds have become valid alternatives to fish meal (FM) and fish oil (FO). While their impact on growth performance is well established, effects on host-associated microbiota remain less characterized. This study examines the gut microbiota of European sea bass (Dicentrarchus labrax) following FM and FO replacement with poultry- and additive-based diets, applying machine learning (ML) to evaluate diet and genotype effects. A secondary analysis of microbial profiles from two prior trials employed classification models to determine associations between microbial abundance and categorical groupings, and regression models to assess the predictive power of ingredient variations on microbial abundance. Regressors showed limited predictive capacity, whereas classifiers performed better, particularly when genotype was considered. For poultry-based diets, average accuracy was approximately 0.4 for synergistic effects, 0.6 for diet effects, and 0.8 for genotype effects; for fortified-feed diets, average accuracy was approximately 0.2, 0.4, and 0.5, respectively. Feature selection detected microbial genera encompassing beneficial (Brevundimondas, Clostridium, Idiomarina, Lactobacillus, Marinobacter, Pseudoalteromonas, Salinisphaera), neutral (Enterovibrio, Flavobacterium, Photobacterium), opportunistic (Acinetobacter, Escherichia-Shigella, Streptococcus), and undercharacterized (Acholeplasma, Cutibacterium, Enhydrobacter, Micrococcus, Peptoniphilus, Salegentibacter) taxa. ML techniques thus reveal diet- and genotype-specific signatures, underlining the importance of integrated computational-microbiological pipelines.