The increasing threat of ransomware attacks demand innovative defense mechanisms capable of real-time detection and mitigation. Traditional signature-based approaches fail against evolving polymorphic variants, while existing machine learning solutions suffer from high false positives and performance overhead. This paper presents a novel AI-driven cybersecurity framework that synergistically combines Random Forest algorithms with Long Short-Term Memory (LSTM) networks for comprehensive ransomware defense. Our hybrid approach monitors multi-dimensional behavioral indicators including file entropy patterns, network traffic anomalies, and process execution sequences through advanced feature extraction techniques. Implemented using Python with TensorFlow and Scikit-learn, the system achieves 98.7% detection accuracy with only 185 ms average latency and 0.8% false positive rate on a dataset of 15,000 ransomware samples across 12 families. The framework incorporates an interactive dashboard for real-time visualization and automated graduated response protocols that effectively contain threats at median 23% encryption completion. Experimental results demonstrate significant improvements over existing solutions, establishing new benchmarks for proactive ransomware defense in enterprise environments.