This study explores the application of deep learning techniques [1], specifically Convolutional Neural Networks (CNNs) [2] and U-Net, for the identification of deforestation using satellite photos. Complex vegetation patterns and cloud interference make it difficult to automate the detection of deforestation through the study of satellite data. The Sentinel-2 collection [3], which offers high-resolution multispectral images perfect for environmental monitoring, is used in this study. Fifty experimental trials tested an amount of 5000 Sentinel-2 images across three different geographical areas. After a great deal of testing, the U-Net model outperformed conventional CNN architectures, obtaining a 95% Intersection over Union (IoU) score [4]. This outcome demonstrates how well the algorithm can identify deforested areas, even in intricate landscapes with diverse vegetation patterns. The findings emphasize the potential of deep learning techniques in improving deforestation detection accuracy and efficiency. By automating large-scale image analysis, these models can significantly enhance environmental monitoring efforts, enabling policymakers and conservation agencies to respond more effectively to forest degradation. This study aims to contribute to the development of scalable and precise monitoring systems that support sustainable forest management and climate resilience.