The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utilized to boost the water-based fluid’s thermal effect. This study primarily focuses on heat transfer over a horizontal sheet, exploring different scenarios by varying key factors such as the magnetic field and thermal radiation properties. The mathematical model is formulated using partial differential equations (PDEs), which are then transformed into a corresponding set of ordinary differential equations (ODEs) through appropriate similarity transformations. The bvp4c solver is then used to simulate the numerical behavior. The effects of relevant parameters on the temperature, velocity, skin friction, and local Nusselt number profiles are examined. It is discovered that the parameters of the Prandtl fluid have a considerable impact. The local skin friction and the local Nusselt number are improved by increasing these parameters. The dataset is split into 70% training, 15% validation, and 15% testing. The ANN model successfully predicts skin friction and Nusselt number profiles, showing good agreement with numerical simulations. This hybrid framework offers a robust predictive approach for heat management systems in industrial applications. This study provides important insights for researchers and engineers aiming to comprehend flow characteristics and their behavior and to develop accurate predictive models.

The assessment of land suitability and capability to reduce the anthropogenic pressure on is significant for the agricultural yield management in rainfed Potwar Region Pakistan. This study examines phases of wheat growth using Landsat-8 thermal bands and temperature based site maps, and demarcates suitable sites in potwar region for sustainable crops through Multi-Criteria Evaluation (MCE), Analytical Hierarchy Process (AHP), python and geospatial techniques. The total investigation area was 22168 km² out of which 12256 km² (55%) declared highly suitable, 2998 km² (13%) was moderately suitable, 4843 km² (22%) was less suitable and 2069 km² (10%) was found not suitable for wheat crop cultivation (WCC). The results indicated that the areas having temperature ranging between 10–18 °C, pH between 6.2–6.5 and clay loom soil type with drainage level 0.85–1.1 corresponds to extremely suitable for wheat production. Various land quality factors; that is slope, flooding, soil texture, erosion, depth and coarser bits under different land-units were assessed for the crops. Consequently, all these factors were combined to make an integrated land suitability and capability (LSC) map for each landuse and suitable soils for agricultural productions. It is concluded that better landuse practices can be implemented to enhance the suitable agricultural sites.