Forest fires have long been a significant threat to ecosystems, biodiversity, and climate stability around the world. The increasing scale and severity of these events, exacerbated by climate change, highlight the need for advanced prevention and response systems. Traditional manual firefighting measures are often delayed, leading to catastrophic outcomes. This paper proposes an original AI-enabled fire response system that combines in-forest sensor networks with automated water or fire extinguishing solutions, aiming for rapid, localized response. Employing smoke sensors and machine learning algorithms, the system detects early fire signs and instantaneously activates localized suppression mechanisms to prevent spread. This research addresses the design, operation, and potential effectiveness of such a system, drawing on literature, technological assessments, and simulations. The findings suggest that AI-driven integrated systems can dramatically enhance early fire suppression and lessen environmental, economic, and health impacts.