In order to effectively manage agriculture and promote sustainable land use in Africa, accurate mapping of irrigated croplands is imperative. This paper pioneers a novel Hybrid Vegetation Index (HVI) and integrated an approach harnessing the synergies between Radar imagery from Sentinel-1 and optical data from Sentinel-2, key vegetation indices (HVI, MSAVI), and Principal Component Analysis (PCA) to significantly enhance mapping accuracy across diverse African landscapes. Employing Convolutional Neural Network (CNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers, the combined potential of these satellite datasets is thoroughly Assessed. The integrated utilization of both Sentinel-1 and Sentinel-2 demonstrates a substantial enhancement in overall accuracy, elevating classification results by approximately 5-6% when compared to individual sensor applications. Specifically, in Kenya, Egypt, and Nigeria, the amalgamation of Sentinel-1 and Sentinel-2 enables CNN to achieve remarkable accuracies of 97.5%, 98.01%, and 98%, respectively. These findings underscore the superior performance of the fused Sentinel data and highlighted the pivotal role of their integration in advancing precise agricultural mapping. This research emphasizes the promising impact of HVI and Sentinel satellite missions in empowering informed decision-making and promoting sustainable land management practices across vital agricultural regions in Africa.