Understanding the maximum percentage of urban area within an administrative region, such as Semarang City, necessitates an examination of spatial planning schemes, development regulations, and local government policies. Concurrently, cellular automata and Markov chain approaches can be used to predict how cities will grow in the future accurately. This study aims to de@ine the urban growth boundary in Semarang City by integrating spatial planning approaches with predictive modeling techniques. The Cellular automata-Markov chain (CA-MC) method predicts future urban growth developments based on current land use patterns. This study seeks to delineate areas suitable for urban development using spatial data analysis and modeling while preserving critical ecological and agricultural zones. The @indings of this research contribute to formulating informed policies aimed at achieving balanced urban expansion and environmental conservation in Semarang, thus fostering resilient and inclusive urban landscapes in the city. © 2025 Institute of Physics Publishing. All rights reserved.