Improving drought endurance in wheat needs high-throughput screening of yield components including seed volume, area, and weight, all of which are very effortful, time-consuming, and visually mostly infeasible. Imaging-based screening presents an opportunity for more exact/rapid analysis of seed morphometric traits. Therefore, this study was aimed at evaluating the phenotypic diversity of wheat seeds under rain-fed and well-watered conditions by using image analysis. From our observations, the criteria Feret (largest axis length) and MaxR (radius of the enclosing circle centered at the middle of mass) exhibited that grain length is less affected by drought. In the rain-fed environment, seed weight had the highest correlation with seed volume (r = 0.76**) and area (r = 0.76**). The correlation of 1000-grain weight with aspect ratio (r = –0.22**) and Rroundness (r = –0.21**) was negative and also non-significant (P > 0.05). The PCA and cluster analysis highlights the MinR (radius of the inscribed circle centered at the middle of mass), Area, Circ (Circularity), ArEquivD (area equivalent diameter), thickness, and seed volume characteristics can be used as useful parameters to identify genotypes suitable for planting in a rain-fed environment. The relative importance of traits for 1000-grain weight in the neural network displayed that the greatest impact in the rain-fed environment was related to seed volume, area, and MBCRadius (radius of the minimal bounding circle). Overall, our findings permitted the formation of a morphometric seed database for the conservation and characterisation of wheat germplasm.