Accurate land use/land cover (LULC) classification is crucial for understanding environmental dynamics, monitoring natural resources, managing urban expansion, and promoting sustainable land management practices. The availability of labeled datasets is a significant obstacle to accurate land use/land cover (LULC) classification in isolated and underrepresented areas like the Barak River Basin. This study presents an unsupervised classification on Landsat 8 satellite imagery, implementing several spectral indices to overcome the insufficiency of the label data set. For vegetation identification, the Normalized Difference Vegetation Index (NDVI), Modified NDVI (MNDVI), Green NDVI (GNDVI), and Ratio Vegetation Index (RVI) were calculated. Water body detection utilized the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Water Ratio Index (WRI), and Automated Extraction of Water Index (AEWI). For built-up area mapping the Normalized Difference Built-up Index (NDBI), Urban Index (UI), and Built-up Index (BI) were evaluated. Amid these, it came to light that NDVI, WRI, and BI performed best for their respective categories. Otsu’s thresholding technique was applied to further process these determined indices in order to classify the binary imagery of the Barak River Basin. Notwithstanding the lack of labeled training data, the thereby generated classification output was evaluated through ground truth verification and accuracy assessment, suggesting excellent performance. Utilizing the highest-performing indices, we were able to generate the label Landsat 8 imagery using an unsupervised method. In areas with inadequate information, this technique makes it possible to develop spatiotemporal datasets for long-term environmental monitoring and land management, and it determines the prerequisites for scalable LULC mapping.