Sustainable crop development depends on understanding the complex interactions between soil, water, and nutrients under dynamic environmental conditions. This paper presents a modelling system designed to explore soil–water–nutrient dynamics for improving agricultural productivity and resource efficiency. The system integrates hydrological, biogeochemical, and crop-growth sub-models within a modular, data-driven simulation framework. It quantifies water fluxes, nutrient transport, and plant uptake across temporal and spatial scales, enabling prediction of yield responses under varying management and climatic scenarios. The model leverages coupled differential equations, mass-balance principles, and machine learning algorithms for parameter optimization and uncertainty reduction. The framework comprises three core modules: (1) Soil–Water Module, which simulates infiltration, evaporation, transpiration, and percolation using Richards’ equation and hydraulic conductivity functions; (2) Nutrient Dynamics Module, which models nitrogen and phosphorus cycling, mineralization, and leaching, incorporating microbial and temperature-driven kinetics; and (3) Crop Growth Module, which links water and nutrient availability with photosynthetic efficiency, biomass accumulation, and phenological stages. These modules exchange data in real time, enabling continuous feedback between soil moisture, nutrient concentration, and plant growth. Calibration and validation employ field data from diverse agro-ecological zones, integrating remote sensing, soil sensors, and weather station inputs. The model applies Monte Carlo simulations and sensitivity analysis to quantify uncertainty and identify key influencing parameters. Scenario-based simulations assess impacts of irrigation schedules, fertilizer regimes, and climate variability on yield and resource use efficiency. Results demonstrate that optimized irrigation and nutrient management can improve water productivity by 20–35% and nutrient use efficiency by 25–40%, while reducing nitrate leaching and greenhouse gas emissions. This modelling system supports decision-making for sustainable intensification, precision agriculture, and ecosystem resilience. It provides a scalable and transferable tool for policy evaluation, agronomic planning, and adaptive management under climate change. By integrating physical processes with economic and environmental indicators, the framework advances holistic resource management and aligns with the United Nations Sustainable Development Goals on food security, water conservation, and climate action.