The escalating growth of urbanization and the increasing complexity of urban issues have made the utilization of novel analytical tools in the urban planning process inevitable. Among these, Artificial Intelligence (AI), as an emerging paradigm, possesses remarkable capabilities in analyzing complex spatial patterns and identifying inequalities in the distribution of urban services. This study aims to investigate these capacities in neighborhood-based planning, focusing on the neighborhoods of Marvdasht city. The core issue is the inefficiency of traditional methods in analyzing the imbalance between population density and service accessibility. The research employs a descriptive-analytical approach based on intelligent modeling (the K-Means++algorithm). Findings indicate a significant gap between high population density in peripheral neighborhoods and the level of provided services. The results of AI optimization models suggest the potential to reduce this gap by 45% over a five-year period. This study concludes that AI is not merely a tool for analyzing the status quo; rather, through scenario planning and intervention prioritization, it provides an efficient platform for transitioning from static to dynamic and justice-oriented planning. Utilizing such systems is a fundamental strategy for enhancing spatial justice in the neighborhood-based management of Marvdasht.