Abstract: Urban–rural integrated development has emerged as a core strategic pathway for achieving coordinated regional development in the new era. It plays a pivotal role in mitigating spatial conflicts among production, living, and ecological functions, while promoting the rational and scientific allocation of territorial spatial resources. This study takes the urban–rural integration development pilot zone in Chongqing, China, as a case study—an area characterized by a complex spatial landscape shaped by the compounded challenges of rapid urbanization, ecological fragility, and the demand for sustainable agricultural development.To explore future land use dynamics, the Patch-generating Land Use Simulation (PLUS) model is employed to construct and simulate three representative land use evolution scenarios: the Natural Development Scenario (NDS), which follows historical trends; the Ecological Protection Scenario (EPS), which emphasizes environmental preservation; and the Farmland Protection Scenario (FPS), which prioritizes agricultural security. These simulations project land use patterns for the year 2030, providing a scientific basis for evaluating trade-offs among competing land demands.Building upon the scenario simulations, the study integrates spatial overlay analysis with a Python-based multi-condition automated trade-off algorithm to develop an innovative three-dimensional spatial regulation framework. This framework consists of three core control layers: the “Farmland Protection Red Line,” which safeguards essential agricultural areas; the “Ecological Security Baseline,” which preserves vital ecological spaces; and the “Development Flexibility Zone,” which accommodates regional growth needs within defined boundaries. Together, these elements aim to reconcile the tensions among ecological conservation, farmland preservation, and development flexibility.Three key findings emerge from the analysis:Land Use Dynamics: All three scenarios reveal that farmland is under dual pressure from both urban expansion and ecological protection mandates. Significant farmland loss is observed in each case—22.57 km² under NDS, 18.83 km² under EPS, and 10.42 km² under FPS. Similarly, forested areas experience degradation across scenarios. Construction land expands most notably under NDS (30.7 km²), followed by EPS (19.7 km²) and FPS (17.42 km²), underscoring the varying impacts of policy priorities.Spatial Conflict Patterns: The study identifies 107 types of spatial conflict overlays among land patches, demonstrating pronounced regional heterogeneity. High-conflict areas such as Jiangjin District (26,687 conflict patches) and Yongchuan District (12,100 patches) contrast sharply with lower-conflict zones like Tongnan District (3,674 patches). These patterns closely align with disparities in local development strategies and resource endowments.Integrated Regulation Effectiveness: The proposed spatial regulation system offers robust multi-objective optimization. Compared to NDS, it results in a 106.57 km² increase in protected farmland, a 112.16 km² reduction in construction land expansion, and a 2.6% improvement in forest retention. These outcomes highlight the framework’s effectiveness in balancing growth with ecological and food security imperatives.This research contributes a novel and empirically validated framework for synergistic “quantity–quality–space” optimization in land use management. It provides a replicable model for territorial spatial planning in transitional and rapidly urbanizing regions. To further enhance its implementation, future work should focus on strengthening ecological compensation mechanisms, integrating real-time data streams with simulation models, and refining legal and institutional safeguards. Additionally, incorporating machine learning techniques may improve parameter calibration, thereby enhancing the framework’s adaptability across diverse regional contexts. This study thus offers valuable theoretical insights and practical strategies for advancing sustainable urban–rural transformation and collaborative governance at multiple scales.