This study introduces a method to improve the accuracy of Land Use and Land Cover (LULC) mapping by addressing the limitations of single-source satellite data. The approach combines data from multiple sources—Landsat-8, Landsat-9, and Sentinel-2 (30m) and applies an ensemble machine learning technique with majority voting. This integration results in a more comprehensive dataset for classification algorithms. The ensemble approach, which combines methods such as Random Forests, Support Vector Machines, and Neural Networks through majority voting, leverages the strengths of multiple predictive models to enhance the reliability and accuracy of LULC classifications. The combination of multisource data merging and the majority voting technique significantly improved LULC mapping accuracy compared to traditional single-source methods, with an overall accuracy increase of 11.28% and a standard deviation of ±6.82%. These findings suggest that integrating multisource satellite imagery with ensemble machine learning models using majority voting is an effective strategy for improving the quality and accuracy of LULC maps.