The increasing interdependence of software, hardware, cloud infrastructures, embedded platforms, and connected systems has transformed cybersecurity vulnerability assessment from an isolated technical activity into a multidimensional security requirement. Software vulnerabilities can arise from insecure code, configuration weaknesses, vulnerable dependencies, and exploitable application interfaces, whereas hardware vulnerabilities may originate from architectural design limitations, processor-level weaknesses, implementation flaws, and hardware-assisted attack mechanisms. This study develops an integrated security analysis model for identifying vulnerabilities across software and hardware system layers. The proposed approach synthesizes vulnerability taxonomy, attack-surface analysis, risk assessment, exploitability evaluation, hardware security analysis, machine-learning-assisted detection, and adaptive defensive mechanisms. The methodology is conceptually grounded in the supplied literature and uses a layered assessment process to connect vulnerability discovery with exploitability, potential impact, and mitigation priority. Particular attention is given to base-image vulnerabilities, processor-level attacks, artificial-intelligence-supported security analysis, HTTPS deployment weaknesses, embedded-system risks, and cyber-hunting relationships between threats and defensive weaknesses. The analysis indicates that isolated software or hardware assessment can overlook cross-layer attack paths, while an integrated model provides stronger contextual understanding of vulnerabilities and their operational consequences. The study further identifies the need for risk-oriented prioritization, continuous monitoring, and coordinated software-hardware defenses. The proposed model provides a conceptual foundation for systematic vulnerability assessment in heterogeneous computing environments.