Wildfires threaten ecosystems, property, and lives, and risk growing due to climate change and human activities, so improved detection and response are urgently needed. This research explores using advanced computer vision techniques to enhance wildfire identification. State-of-the-art models including Convolutional Neural Networks, EfficientNet, Vision Transformers, Swin Transformers, and Data-efficient Image Transformers are applied to classify wildfire occurrences in diverse image datasets accurately. Traditional wildfire detection has limitations that modern machine learning can help address through superior spatial and temporal capabilities. A comparative study of these models not only examines individual performance regarding accuracy, precision, recall, and F1-scores but investigates ability to handle complexity and variability in wildfire images. Analyzing model performance aims to contribute to developing more effective real-time wildfire monitoring and response systems. Through thorough experimentation and assessment, findings provide environmental monitoring and disaster management insights into artificial intelligence potential, priming future optimization for real-world use while ensuring generalization across datasets and conditions.