Fire hazards pose a significant risk to human lives, infrastructure, and the environment, necessitating the development of efficient fire detection and localization systems. Traditional methods, including smoke and heat sensors, suffer from high false alarm rates and delayed response times. In recent years, deep learning has emerged as a transformative approach, leveraging computer vision to enhance accuracy in fire detection and localization. This paper provides a comprehensive survey of deep learning techniques employed for fire detection, including Convolutional Neural Networks (CNNs), object detection models such as YOLO and Faster R-CNN, and hybrid approaches integrating multimodal data. The study further explores publicly available fire datasets, preprocessing techniques, and performance evaluation metrics. Additionally, key challenges such as real-time processing constraints, environmental variability, and model generalization are discussed. The paper concludes with an outlook on future advancements, including lightweight AI models, multi-sensor fusion, and synthetic dataset generation for robust fire detection systems.