Forest fires pose significant threats to forest ecosystems, impacting humans, animals, and plants reliant on these environments. Traditional detection methods rely on handcrafted features like color, motion, and texture, yet achieving accuracy remains challenging. This study introduces a novel approach using a lightweight fire detection method employing Deep Convolution Neural Networks (DCNN), considering temporal aspects for enhanced accuracy. By leveraging DCNN, this study aims to improve forest fire detection capabilities, mitigating the devastating effects of wildfires on both natural habitats and communities. This method represents a promising advancement in the field, offering potential solutions to the ongoing challenge of timely and accurate forest fire detection.