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• Novel Methodology Integration: Introduction of a novel methodology combining Geographic Information System-based fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and filtration algorithms. • Extensive Evaluation of Solar Energy Sites (SES): Evaluation of 19,237 coordinated data points for SES potential based on 12 diverse criteria, including climatic conditions, technical feasibility, accessibility, environmental considerations, and social factors. • Double Filtration Process: Implementation of a meticulous double filtration process, systematically eliminating unsuitable locations and resulting in a refined list of 1,862 potential SES. • Fuzzy TOPSIS Algorithm Selection: Utilization of the fuzzy TOPSIS algorithm to further narrow down the SES selection to the top 100, with closeness coefficients ranging from 0.7 to 0.85, highlighting optimal solutions. • Spatial Distribution Analysis and Model Validation: Presentation of an interesting spatial distribution of the top 100 SES, clustered in three major categories, and validation of the proposed model through a solar suitability map in ArcGIS Pro, demonstrating a 69.01 % average relative percentage consistency with the model's results, similarly, the code used is adaptable to various polygon layers and raster maps, serving as a valuable resource for researchers conducting large-scale solar energy site identification in future studies. Solar energy stands as an increasingly attractive and environmentally responsible energy source, possessing the potential to meet the burgeoning power needs of the Sarawak region. This paper proposes a novel methodology that combines Geographic Information System-based fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and filtration algorithms to identify the top 100 optimal Solar Energy Sites (SES). The research encompasses the extra-large scale potential SES comprising 19,237 coordinated data points. It evaluates SES based on 12 diverse criteria spanning climatic conditions, technical feasibility, accessibility, environmental considerations, and social factors. This research employs a meticulous double-layer filtration process to systematically eliminate unsuitable locations, resulting in a refined list of 1,862 potential SES. Subsequently, the fuzzy TOPSIS algorithm has further narrowed down the selection to the top 100 SES, constituting the core results of this research. The results reveal an interesting distribution of the identified SES, which mainly clustered in 3 huge categories (Kuching-Samarahan, Sibu-Kapit-Mukah and Bintulu-Miri). The identified 100 SES possess high closeness coefficients, which range from 0.7 to 0.85, where 1 indicates the best solution of the fuzzy TOPSIS algorithm. Additionally, the paper presents a spatial distribution analysis of the top-ranking sites across various divisions and districts. To validate the results obtained from the proposed model, a solar suitability map has been developed in ArcGIS Pro. When comparing the results from the proposed model to those obtained from the solar suitability map, most SES have consistent relative percentages, with only one SES falling below 50 %. To provide an overview of the similarity between the two models, the average relative percentage is computed at 69.01 %, signifying the proximity of SES to the optimal value of the solar suitability map. Furthermore, the developed source code in this research exhibits flexibility, featuring filtration algorithms that can accommodate any raster maps. It serves as an asset for researchers interested in conducting comprehensive large-scale SES identification in future case studies. [ABSTRACT FROM AUTHOR]