Abstract: This study explores the latest advancements in wildfire spread detection and forecasting techniques, focusing on the complex challenges and practical applications in this field. We provide an exhaustive review of state-of-the-art methods and technologies for detecting and predicting wildfires. We examine the fundamental principles of wildfire behavior and spread dynamics, remote sensing (RS) methodologies such as satellite imagery and unmanned aerial vehicles (UAVs), and the role of artificial intelligence (AI)-driven approaches such as machine learning (ML) and deep learning (DL) algorithms in enabling rapid analysis of vast datasets. We investigated predictive modeling approaches that integrate various environmental factors, topographical features, and fuel characteristics to forecast wildfire behavior and spread patterns. Real-world case studies illustrated the practical implications of these technologies in wildfire management and emergency response efforts. We also addressed the challenges and limitations of current wildfire detection and prediction systems, including data accuracy, computational resources, and operational constraints. Our target audience includes researchers, practitioners, policymakers, and stakeholders in wildfire management, environmental science, and disaster preparedness. Ultimately, we contributed to the literature by offering a comprehensive review of advances in wildfire spread detection and prediction, addressing key challenges, showcasing practical applications, and outlining future research directions to foster collaboration and innovation in wildfire management efforts worldwide.