This study explores how Geographic Information Systems (GIS) can be enhanced through the integration of big data, data science, and machine learning techniques. It highlights the role of data-driven approaches in overcoming the limitations of traditional GIS methods.This provides  new dimensions to spatial data analysis. The study focuses on the utilization of machine learning and deep learning techniques for processing and analyzing big data obtained from various sources such as satellite imagery, sensors, and social media. Application areas such as urban planning, disaster management, environmental monitoring, and transportation analysis are discussed as examples. Additionally, the study examines the advantages of integrating augmented reality (AR), real-time data analytics, and cloud-based solutions to GIS. These technologies are shown to have significant potential in areas such as city planning, traffic management, and monitoring environmental changes. The importance of data visualization tools and techniques in facilitating the interpretation of spatial data and supporting decision-making processes is emphasized. Finally, the study addresses existing challenges, including data quality, integration issues, and high computational costs, while discussing future trends such as AI-powered models and cloud-based solutions.