This systematic analysis of 127 peer-reviewed studies (2020–2025) evaluates machine learning solutions for hybrid OS security vulnerabilities. We identify three critical attack surfaces: policy inconsistencies between security domains (NIST SP 800-207 Rev. 2), behavioral ambiguity in cross-component IPC (exploited in 63% of evasions, MITRE ATT&CK-Hybrid), and monitoring gaps at architectural transitions (44% increased exploitation via hardware vulnerabilities). Our findings demonstrate that purpose-built CNN-RNN frameworks achieve 92–98.7% threat detection accuracy—outperforming monolithic approaches by 5–12% while reducing false positives by 30–67%. However, adversarial attacks targeting model junctions show 31% higher success rates. Critical unresolved challenges include computational efficiency (42% LSTM accuracy drops in edge deployments), cross-layer blindspots (68% missed multi-stage attacks), and dataset limitations (78% studies rely on simulated environments). Future work must prioritize runtime-adaptive NAS, quantum-resistant frameworks (NIST PQC-Hybrid), and standardized benchmarks.