Ransomware assaults surged by over 93% in 2022 only, with losses forecast to top $265 billion per year by 2031 [1], [2]. These attacks are progressively aiming at fiscal organizations, software-as-a-service (Saas) systems, as well as essential architecture utilizing advanced payload deployment, stealth security, and multi-vector matrix coercion. Notwithstanding major research into timely detection combined with restoration methods, several defenses persist reactive, fragmented, or unable to detect to firmware-level as well as AI-enhanced threats. This paper offers ML-RDM — a Multi-Layered Resilient Defense Model — to handle these challenges. The presented framework includes immediate behavioral monitoring, system software diagnostics, AI-based coordination, previously isolated records secure location, together with human-behavior link into a harmonized, responsive structure. This model is validated theoretically through scenario mapping of five major incidents along with benchmarked against 20 contemporary investigation works. Evaluation demonstrates reduced time-to-detection, improved repair completeness, combined with strengthened false-positive suppression. ML-RDM is positioned as a forward-compatible answer, architected for dynamic ransomware ecosystems and adaptable across finance, enterprise IT, together with hybrid cloud environments.The ML-RDM framework presents a hybrid paradigm emphasizing actual threat containments, decentralized restoration, as well as policy-aligned escalation-utlitmately aiming to future proof digital ecosystems against continually intelligent encryption malware variants.