Understanding climate change’s possible effects on sea surface temperatures (SSTs) is vital for marine ecosystems. For the first time, the potential effects of climate change on SSTs were investigated using the SST data from five meteorological observation stations located along the Black Sea coast of Türkiye, covering the period 1963–2014. The SSP2-4.5 and SSP5-8.5 scenario outputs of the general circulation models, namely CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, and NORESM2-MM, selected from the Coupled Model Intercomparison Project Phase 6 archive were downscaled to station scale by conventional regression analysis and artificial neural networks as statistical downscaling method in the study. SST values for the future periods (2023–2052 and 2053–2082) were obtained using the ensemble model approach for each station. Additionally, quantile delta mapping was used for bias correction. Three performance metrics were used to evaluate the downscaling performance of the methods. The model with the best performance was selected, and the future SST results were obtained and compared with the historical observation data. It was projected that monthly SSTs may increase by 1.66 °C–4.25 °C under the SSP2-4.5 scenario and 1.71 °C–5.75 °C under the SSP5-8.5 scenario. This increases may cause the deterioration of the marine ecosystem and local economic downturn in the Southwestern Black Sea.