Oman, a country in Southwest Asia, situated on the southeastern quarter of the Arabian Peninsula presents a high level of biological diversity especially marine elements. Although arid habitats cover most parts of Oman (82%), the region has several freshwater systems that are vital for the survival of people as well as for different groups of animals and plants. Research works on Oman biodiversity including terrestrial and marine, have been steadily increasing over the last few decades, but freshwater ecosystems have not been well investigated. Oman comprises parts of three freshwater ecoregions including the Oman Mountains, Southwestern Arabian Coast, and Arabian Interior having xeric freshwaters and endorheic (closed) basins which support a variety of inland fishes. The current checklist provides for each species of inland waters of Oman all recognized and named taxa, documenting recent changes and controversies in nomenclature, its records, taxonomic status, synonyms, etymology, common English name, short description, range expansion, and detailed distribution map based on several field surveys throughout the country. We also provide native, endemic, and introduced species. The diversity of inland fishes of Oman included in this annotated checklist consists of 23 recognized species in 15 genera, 10 families, seven orders, and a class. Also, for the first time, we report and confirm the presence of four species in the inland waters of Oman. The most diverse order is Cypriniformes (nine species, 39.13%), followed by Gobiiformes (six species, 26.09%), Cyprinodontiformes (three species, 13.04%), Cichliformes (two species, 8.69%), and Centrarchiformes, Gonorynchiformes and Mugiliformes (one species, 4.35% each). 21 native species (91.3%) in nine families and two exotic species (8.7%) in two families are listed here. Out of 21 native species, eight species (16.8%) in two families are endemic elements that are restricted to the Oman territory only. Identification of all recognized species was confirmed by DNA barcoding (mitochondrial COI). Oman Mountains Ecoregion (OME), Southwestern Arabian Coast Ecoregion (SACE), and Arabian Interior Ecoregion (AIE) harbor 15, 12, and one species, respectively. The provided data will be necessary for increasing the fish knowledge, the development of competent and pragmatic management plans and effective conservation policies.

Garra is the most species-rich genus of freshwater fishes in the Arabian Peninsula, including Oman, which has eight recognized, species restricted mainly to the Hajar and Dhofar mountains. Garra species from the Dhofar region in Oman are reviewed based on integrated morphological and molecular (COI) data of newly collected fishes from the type localities of G. sindhae and G. smartae, and a new population from Wadi Laggashalyon. No diagnostic character was found to distinguish the two species and the Laggashalyon population as distinct taxa. Garra dunsirei, another species endemic to a sinkhole at Tawi Attair, a very difficult area to access at a depth of about 211 m, was examined here based on the GenBank data and the diagnostic characters given in the original descriptions of the studied Garra species. In addition, the phylogenetic tree placed the sequenced Omani Garra fishes into nine groups with K2P sequence divergence of 0.8% (G. smartae and G. smartae_Laggasaylon); 1.2% (G. smartae and G. sindhae), and 14.5% (G. Shamal and G. dunsirei) in their COI barcode region. A molecular species delimitation approach using PTP, also detected one entity for Garra species from the Dhofar region (G. dunsirei, G. sindhae and the both populations of G. smartae), which is consistent with our morphological data. Therefore, based on absence of diagnostic characters, low genetic distance, and molecular species delimitation approach, we treat G. sindhae and G. smartae as junior synonyms of G. dunsirei.

AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems.