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Forest Fires Are One Of The Most Destructive Natural Disasters, Causing Significant Ecological Damage, Economic Loss, And Threats To Human Life. Early Detection And Accurate Classification Of Forest Fire Incidents Are Crucial For Effective Fire Management And Mitigation Strategies. Traditional Fire Monitoring Systems, Which Rely On Manual Observation And Sensor-based Techniques, Are Often Time-consuming, Expensive, And Limited In Scalability. Recent Advancements In Machine Learning (ML) And Deep Learning (DL) Have Opened New Opportunities For Automated Forest Fire Detection Using Image Classification Techniques. In This Study, We Propose A Comparative Framework For Classifying Forest Fire Images Using Both Traditional Machine Learning Algorithms And Deep Learning Architectures Such As Convolutional Neural Networks (CNNs). Machine Learning Models Are Employed With Handcrafted Features, While Deep Learning Approaches Automatically Extract Discriminative Spatial Features From Raw Images. The Performance Of Different Models Is Evaluated Using Accuracy, Precision, Recall, And F1-score Metrics On Benchmark Datasets. Experimental Results Demonstrate That Deep Learning Methods Significantly Outperform Traditional Machine Learning Techniques In Terms Of Classification Accuracy And Robustness, Particularly Under Complex Environmental Conditions. The Findings Highlight The Potential Of Deep Learning–based Image Classification Systems As Effective Tools For Real-time Forest Fire Detection, Supporting Rapid Response And Disaster Management Efforts.