This study utilizes integrated innovative approaches in machine learning modeling to analyze the Earth’s surface and examine urban expansion resulting from human activity, focusing on land use/land cover (LULC) changes in the Mashhad Metropolitan Area (MMA) from 1990 to 2020, with predictions for 2030. Advanced algorithms, including support vector machines (SVMs), Multilayer Perceptron (MLP) neural network, and Markov chains, were employed in the analysis. The results indicate that from 1990 to 2010, built-up areas increased by 51.70%, green spaces by 26.40%, while sand-covered areas decreased by 36.49%. From 2010 to 2020, the growth rate for built-up areas and green spaces slowed, with built-up areas growing by 13.27% and green spaces by 12.29%. The findings project a 15.59% increase in built-up areas and a 6.44% decrease in green spaces by 2030. Model validation was conducted using the Area Under the Curve (AUC) of 0.767 and Kappa coefficient of 0.7589, indicating strong model validity. Furthermore, this research identifies critical factors influencing LULC changes, revealing that the distance from green spaces and proximity to built-up areas are the most significant determinants. By addressing a significant gap in understanding the impact of human activities on urban dynamics and their ecological implications in developing regions such as the MMA, this study contributes meaningfully to urban planning.

Building density is a management instrument that regulates urban development and balances urban areas' spatial and functional structures. It is recognized as a qualitative component of urban form and landscape. Consequently, effective spatial management of urban elements, functions, and activities necessitates the assessment of building density capacity within cities. This research aims to utilize deterministic Boolean and fuzzy multi-criteria decision-making methods, alongside the Analytic Hierarchy Process (AHP), to evaluate the loading capacity of building density in the  Qaem Shahr city of  Iran. The methodology employed is developmental-applied, framed within an analytical-descriptive approach. To enhance the validity of the findings, a combination of Boolean and fuzzy analytical techniques has been integrated with hierarchical analysis using ArcGIS. This analysis focuses on four criteria: physical-spatial, environmental, social, and economic, along with their respective components. The Boolean and fuzzy logic analysis results indicate that the city center and the northern section of the central thoroughfare are entirely suitable for increased density. In contrast, the southeastern and northwestern outskirts are deemed unsuitable. Furthermore, physical-spatial and economic indicators have been identified as the most significant factors influencing density capacity assessment in Qaem Shahr .