Accurate land cover classification is crucial for environmental monitoring, precision agriculture, and sustainable resource management, but traditional methods often struggle to distinguish between spectrally similar classes like different vegetation types, water bodies, and urban areas. This study presents a deep learning-based framework for vegetation analysis using hyperspectral satellite imagery from the USGS Earth Explorer, focusing on the Nagpur region in India. The data is processed and analyzed using QGIS along with open-source tools such as EnMAP-Box, Orfeo Toolbox, and GDAL. Deep learning models are trained to classify land cover types—forests, farmlands, urban areas, and water bodies—and their performance is compared to conventional classifiers like SVM and KNN, showing significant improvements in accuracy and scalability. The framework has practical applications in areas such as precision farming, deforestation tracking, urban green space management, and water quality assessment. Results demonstrate that deep learning effectively captures subtle spectral differences, enabling more accurate classification and early detection of vegetation stress. Future work will aim to scale this system to larger regions and implement real-time monitoring using DSP-based technologies.