Forest fires present significant threats to both natural ecosystems and human communities, highlighting the need for advanced detection systems for early intervention and mitigation. This paper aims to develop a novel forest fire detection system by integrating Internet of Things technology, machine learning algorithms, and real-time data from weather APIs. The proposed system utilizes IoT sensors to gather environmental parameters and weather conditions, enhancing the accuracy of fire detection. A machine learning model trained on this data distinguishes between normal environmental fluctuations and signs of fire. Additionally, an image processing algorithm is employed to analyze images for the presence of smoke or flames. Integration and testing of the system demonstrate its promising results in terms of accuracy and efficiency compared to traditional methods. This paper contributes to technology-driven solutions for forest fire management, with significant implications for environmental conservation and public safety.