Small and medium enterprises (SMEs) form the backbone of global economies, driving innovation and jobs. With the momentum of digitalization, many SMEs are adopting cloud computing and artificial intelligence (AI) technologies to enhance operational efficiency and competitiveness. With these benefits come heightened cybersecurity risks. SMEs lack the financial resources, trained personnel, and official security implementations to defend themselves against more recent threats such as data breaches, ransomware, cloud misconfigurations, and adversarial AI attacks. Therefore, SMEs are high targets for cybercriminals exploiting poor digital defenses. This work presents a customized cybersecurity architecture for the operational circumstances and constraints of SMEs employing cloud and AI-driven services. This architecture builds upon available standards like the NIST Cybersecurity Framework, ISO/IEC 27001, and Zero Trust Architecture and integrates them into a multi-layered, scalable architecture. Key functional areas include risk assessment, identity and access management, AI lifecycle security, data protection, incident response, and regulatory compliance. A mixed-methods approach is employed to balance intellectual rigor and practical significance. Qualitative data are initially collected through expert interviews and case study of recent cyber-attacks on SMEs. A survey of 50 SMEs across different industries (e.g., healthcare, retail, and finance) then quantitatively measures the prevailing cybersecurity maturity and gaps in safeguarding clouds and AI. Shared vulnerabilities found include poor access control, lack of AI-specific security, and zero employee training. On the basis of evidence accrued hitherto, the proposed framework is detailed and tested in a pilot implementation in three SMEs with different models of operation. Key performance indicators e.g., threat detection rate, time to respond to incidents, and compliance level—are tracked for three months. Post-implementation results show significant enhancement in detection potential (up to 45%), reduced mean time to respond (60%), and enhanced conformity with regulatory norms. One of the distinguishing contributions of this work is that it addresses the security of the AI lifecycle, an aspect that typically gets neglected in the traditional SME cybersecurity methodology. The framework encompasses defenses against attacks such as data poisoning and model inversion and encourages transparency, ethical use of AI, and ongoing model verification. Furthermore, the framework also emphasizes risk-based prioritization, allowing SMEs to implement security controls stepwise based on their own business environment, threat landscape, and resource condition. The research fills a critical knowledge gap in the body of cybersecurity literature by offering a simple, flexible, and cost-effective solution to SMEs to respond to complex digital environments. It also provides actionable advice for policymakers, cloud providers, and SME organizations who want to promote secure digital transformation. By assisting SMEs in integrating cloud and AI technologies without compromising on security, the proposed framework facilitates resilience, innovation, and trust in the digital economy. Future research may examine automating this model via orchestration and extending it to new domains such as edge computing and federated learning. Overall, this work contributes a timely, pragmatic model that helps SMEs bridge the cybersecurity capability gap and operate securely in a more AI-centric, cloud-oriented world.