Precise crop classification is crucial for effective agricultural monitoring while current remote-sensing-based crop classification models leading to poor performance across different regions. This research focuses on the classification of crops in Agiripalli, a rural area on the outskirts of Vijayawada in Andhra Pradesh, using advanced remote sensing techniques utilized ML and DL models. Agriculture in this region is marked by diverse crop types and complex planting patterns, which present significant challenges for traditional crop classification methods. By leveraging Sentinel-1 and 2 satellite data through the GEE platform, we aim to develop a robust approach that enhances the precision of crop mapping and supports efficient agricultural resource management. Our methodology involves the application of both random forest (RF) and deep neural networks (DNN) to address the limitations of conventional methods. The random forest model demonstrates exceptional performance within the Google Earth Engine, achieving an overall accuracy of 98%. It also yields a macro-Average precision of 0.86, recall of 0.72, and an F1-score of 0.77, while the weighted average precision is 0.82, recall is 0.80, and the F1-score is 0.79. In parallel, the deep neural network model provides high precision scores, ranging from 0.91 to 0.95, with recall values between 0.50 and 0.94 and F1-scores from 0.65 to 0.81 across multiple crop classes. These outcomes highlight the advantages of integrating both optical and radar data to achieve superior classification accuracy, even under challenging environmental conditions. By optimizing crop identification and enhancing decision-making processes, this research contributes to more sustainable agricultural practices in Agiripalli, enabling better resource allocation, reduced wastage, and overall improved productivity.