This research paper titled “Enhanced Wildfire Detection using AI/ML: Harnessing Multi-spectral Satellite Imagery with Convolutional Neural Networks” aims to advance the capabilities of wildfire detection by employing Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs). Given the escalating threat of wildfires exacerbated by climate change and human activity, traditional detection methods, though effective, are both costly and time-consuming. To counter these limitations, the study taps into multi-resolution satellite imagery, particularly from the VIIRS and Sentinel-2 satellites. The primary data source, VIIRS, offers comprehensive spectral bands and frequent global coverage. In contrast, Sentinel-2 provides high-resolution optical image data vital for detailed wildfire detection. The research processes the collected data, refining and categorizing them for training and testing. A Convolutional Neural Network is then employed to classify images as either “fire” or “nofire.” Two main architectures, Deep CNN and a simplified MobileNet-like CNN, were explored. Among the models tested, the Deep CNN using the Adam optimizer was found to be the most accurate, although it hinted at possible overfitting. The paper also points out several limitations, such as reliance on the visible spectrum that could be obstructed by atmospheric conditions and the temporal gaps in image captures that could delay real-time detection. The study concludes by emphasizing the transformative potential of integrating AI with satellite technology for early wildfire detection. Future advancements could harness multispectral bands and refine spatial and temporal resolutions to further enhance the early detection and intervention of wildfires. The research received support from the Network of Resources (NoR) at ESA, which facilitated expanded access to the SentinelHub platform.