Sentinel-2A remote sensing satellite system was recently launched, thereby providing free global remote sensing data in a similar way to Landsat systems. The mission enables the acquisition of 10 m spatial resolution global data; however, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban area in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets by use of Wavelet transform (WT), Brovey transform (BT), and principal component analysis. Pixel- and object-based classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentiel-2A data provide superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77% by SVM, followed by 65.27% using DT. BT produces the highest OA for the fusion images of 78.40% with SVM and 52.21% with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33% by SVM, followed by 76.38% using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35% with SVM, followed by 65.66% with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and is useful for future long-term mapping applications