Fire detection is vital for safeguarding both the environment and human life; however, conventional fire detection methods, such as temperature and smoke sensors, often face inherent limitations. The machine learning models, specifically convolutional neural networks (CNNs), are commonly used for processing images to enhance the accuracy and efficiency of fire detection, enabling these systems to detect fire in real-time. We propose the YOLOv8 (You Only Look One Version 8) model capable of analyzing high-resolution images of both fires and non-fire scenarios from diverse sources. His study proposes YOLOv8 for real-time fire detection by analyzing high-resolution images from diverse scenarios. Using this algorithm, it can make highly accurate predictions. The Fire dataset is typically split into 80% training, 15% validation, and 5% testing to ensure the model learns effectively. The accuracy of bounding box predictions during training starts at around 0.54 and progressively decreases to about 0.22, and the recall starts at around 0.94 and steadily increases to about 0.99; the loss decreases from about 0.88 to 0.76, showing that the model is making fewer errors in bounding box regression on the validation data, a significant decrease in loss values, suggesting effective learning and optimization, while high and improving precision and recall demonstrate its strong detection performance. The consistently increasing mAP (mean average precision) values further indicate that the model is accurately predicting bounding boxes and classifying fire objects, making it a robust solution for fire detection tasks. Future work will focus on improving data and models and integrating this approach with systems like firefighting control. One of the potential applications for the system includes hotels, factories, and airport safety systems.