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Neoteric phenomena such as climate change, scarce water availability and excessive fertilizer usage necessitate an augmentation of resource utilisation efficiencies in the agricultural sector. There is a need to reorient the agroecosystems to curb stress on environmental resources while meeting rising socio-economic objectives under changing hydro-climatic conditions. Considering this, optimal land allocation for diversified agriculture is essential. We propose a combinatorial optimisation approach for land allocation considering agronomic, socio-economic, environmental and hydro-climatic objectives using bio-inspired optimization algorithms. The stochastic approach tackles the problem of optimal agricultural land allocation for crops in a multidimensional context by simultaneously addressing the conflicting goals of farm-level risk management as well as district-level contingency planning. The efficiencies and sensitivity of the proposed framework are assessed through a case study of the Dharwad district in Karnataka, India using the data (water and fertilizer consumption and cost, crop type, cultivable land, man and machine hours, etc.) from the year 2019–2020. Results indicate that Multi-objective Genetic Algorithm (MOGA) is more capable of optimising agricultural resources management by suggesting optimal land allocation for diversified crop planning. Although Cuckoo Search (CS) and Particle Search Optimisation (PSO) also produced productive Pareto fronts, they were observed to be less effective than MOGA. The annual increase in profits and crop yield obtained using MOGA are 103% and 97% respectively, while water usage is reduced by 5% compared to the conventional routines in Dharwad. The proposed hydro-agronomic decision support framework (DSF) can be utilised to assist the AI-enabled crop planning process for the sustainable management of agroecosystems. [Display omitted] • Artificial intelligence (AI) framework for multi-scalar diversified agriculture. • Socio-economic, agronomic, hydro-climatic and environmental objectives for improving profits. • Comparison of MOPSO, MOGA and MOCS with traditional approach. • Multi-objective genetic algorithm improves profit and crop yield. [ABSTRACT FROM AUTHOR]