Here future spatial layout of cultivated land was investigated under the influence of various spatial factors in the city of Changzhou in the Yangtze River Delta. The cultivability of land resource was evaluated to integrate the urban development potential, habitat conservation potential, and the degree of agglomeration. A machine learning-based model was constructed to assess the land cultivability using multiple algorithms. CatBoost also demonstrated the highest accuracy of classification. The PLUS and InVEST model were then used to calculate the urban development and habitat conservation potential, respectively. Finally, the resulting sub-objectives were input into an Ant Colony Optimization (ACO). As such, the multi-objective spatial allocation of cultivated land was simulated to delineate the optimization zones. The results show that: 1) Training labels were set using NPP (Net Primary Productivity), elevation, and slope in the land cultivability assessment, considering the influencing factors, such as topography, soil, climate, location, and irrigation. CatBoost was outperformed the rest in the accuracy of classification. The evaluation of cultivability revealed that Changzhou shared a potential cultivated land area exceeding the current cultivated land by 543.61 km², indicating the potential for further cultivation. However, 168.25 km2 of the current cultivated land was located in uncultivatable zones, while 254.11 km² of highly cultivatable land remained unused, indicating the significantly spatial imbalances. 2) Changzhou's urban development and habitat conservation potential were calculated using the PLUS and InVEST models, respectively, in order to manage land use conflicts during the optimization of cultivated land layout. The urban development potential exhibited both central outward expansion and road-oriented diffusion. The habitat conservation potential showed the high potential in water bodies and forests, while the low potential in built-up areas. Different weight values were set for the sub-objectives in the utility function. The optimal spatial allocation of cultivated land was simulated under various scenarios. Neglecting degree of agglomeration resulted in the distribution of fragmented land, while the lacking on habitat conservation led to the unreasonable occupation of ecological areas, such as mountains and lakes. The urban development potential was lacking on the substantial cultivated land within core urban development zones. The most optimal layout was achieved to balance the scenario, such as the urban development, habitat conservation, and intensive farming yielded. An average cultivability potential of 0.9387 was obtained for a rational and efficient distribution of cultivated land resources. 3) The spatial allocation was integrated with the current status of land use. A zoning strategy was proposed to maximize the natural endowments of cultivated land, in order to effectively coordinate with urban economic development and ecological conservation. Five zones were divided into the area of core conservation, quality improvement, potential reserve, construction buffer, and ecological conservation. Management strategies were tailored for each zone to enhance the efficient use for the optimal management of cultivated land resources. This finding can provide the new pathway and insights to optimize the cultivated land layout at the patch scale. Regional cultivated land resources were reassessed to effectively mitigate the mismatches of land resource. A comprehensive approach was offered to future spatial layout and management strategies for the cultivated land under multiple spatial factors. The technical insights can be expected to re-evaluate the regional cultivated land resources for the sustainable development.