The proliferation of ransomware attacks across global systems has necessitated the development of advanced detection techniques capable of adapting to increasingly sophisticated threat behaviors. The Deep Ransomware Fingerprint Mapping (DRFM) framework introduced in this work utilizes a novel approach, integrating behavioral and binary fingerprinting within a multi-layered machine learning architecture to provide robust, automated ransomware detection. DRFM leverages convolutional and recurrent neural networks alongside ensemble techniques, enabling the system to capture intricate patterns associated with ransomware activity, including novel and obfuscated variants, through autonomous classification mechanisms. Experimental evaluations demonstrate DRFM's high detection accuracy across known and zero-day ransomware variants, with superior adaptability to evolving encryption and evasion tactics compared to traditional models. Additionally, the framework's consistent memory utilization and low-latency operation under heavy data loads demonstrate its practicality for real-time, largescale cybersecurity applications. DRFM's effectiveness in reducing false positive rates, coupled with efficient resource utilization, highlights its value as a scalable solution capable of enhancing proactive cybersecurity defenses. Through its comprehensive, autonomous design, DRFM establishes a significant advancement in ransomware detection, supporting the increased demand for reliable, automated systems in today's cybersecurity landscape.