Forest fires are a growing global concern, driven by climate change, human activities, and environmental degradation. Traditional fire prediction models, while useful, often fall short in addressing the complex, nonlinear, and dynamic nature of fire behavior. This chapter explores the transformative role of artificial intelligence (AI) and machine learning (ML) in forest fire prediction, focusing on their ability to integrate and analyze diverse, multi-modal datasets such as satellite imagery, weather forecasts, and historical fire records. Key AI/ML techniques, including supervised learning methods like regression and classification, unsupervised clustering approaches, and advanced deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer architectures, are discussed in detail. The chapter also highlights the integration of hybrid models, such as Physics-Informed Neural Networks (PINNs) and multi-task learning systems, to improve predictive accuracy by combining data-driven methods with domain knowledge. Challenges in data preprocessing, heterogeneity, and model interpretability are addressed alongside emerging trends, including Explainable AI (XAI) and federated learning. Through practical case studies and comparative analyses, this chapter provides actionable insights into the application of AI/ML for wildfire prediction, offering a comprehensive resource for researchers and practitioners aiming to enhance fire management strategies, mitigate risks, and protect ecosystems.