Crop yield estimation has significant importance for policy makers to make timely decisions on import/ export of particular crop. Another method for determining vegetation health and yield is the use of satellite imagery. Although several vegetative indices are being utilized, it is unknown how effective they are at estimating yield. This study compared several satellite-based vegetation indices, including the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Modified Soil Adjusted Vegetation Index (MSAVI), to determine which index is most appropriate for the central Punjab, Pakistan cropping area. The research focuses on analyzing the correlation between these vegetation indices and biomass/biological yield and grain yield. Through scatter plots and regression analyses, the study reveals strong positive correlations between these vegetation indices and crop yields, demonstrating their effectiveness as indicators for predicting agricultural productivity. SAVI and MSAVI showed high reliability in semi-arid regions by minimizing soil brightness effects. EVI, with its additional correction for soil and atmospheric influences, proved particularly effective in densely vegetated areas. NDVI also showed a significant correlation with crop yield but was found to be less effective in regions with sparse vegetation due to its sensitivity to soil reflectance. The results revealed that all vegetation indices have a positive correlation with wheat yield, but their predictive power varies. Model-1 of (Wheat Grain Yield), which incorporates all four indices, showed the best performance with an R-squared of 0.91 and a Pearson correlation of 0.95, indicating a strong fit to the observed data. However, its NSE value of 0.89 suggests moderate predictive reliability. Among the vegetation indices, NDVI emerged as the most significant predictor of yield due to its high positive coefficient in the regression models. These findings suggest that the appropriate selection of vegetation indices, considering environmental context, is crucial for accurate yield prediction.