This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data.