Urban growth is one of the major challenges of the twenty-first century, the rapid expansion of urban areas more means complex patterns of land use, infrastructure development, and environmental impact. Analyzing and anticipating these dynamics are fundamental for efficient urban planning, resource management, and policy formulation. Leveraging machine learning (ML) techniques with geographic information systems (GIS) for urban growth patterns prediction. This paper examines integrating machine learning (ML) techniques with geographic information systems (GIS) for urban growth patterns prediction. Leveraging the spatial analytical strengths of GIS combined with the predictive potential of ML, we develop a framework for forecasting urban growth using historical data, socioeconomic parameters, and spatial features. We process ML methodologies such as decision trees (DTs), support vector machines (SVMs), random forests (RFs), and deep learning models for urban growth prediction. We also discuss how satellite and remote sensing data offer high-resolution spatial data collections, which are critical for accurate modeling. Positioned around the synergy between ML and GIS, this work provides a framework for addressing critical urban growth challenges and sets the stage for smarter, data-driven decision making across the fields of urban planning and policy. These findings are intended to be part of the larger conversation around the phenomena of urbanization and thus should be of interest to academics, planners, and public policy-makers operating at the nexus of technology and urbanism.