<p>Cloud computing has emerged as a widely adopted paradigm for delivering flexible and scalable services in information and communication technology. Despite its benefits, the optimal selection of reliable services in dynamic cloud environments remains a major challenge. To address this issue, we propose a novel framework called MRM-C (Multiple Reliability criteria and Markov chain model for Cloud service selection). The framework integrates multiple reliability-related parameters—such as availability, response time, and failure rate—into a Markov chain model that dynamically predicts the most reliable cloud service. A utility-based evaluation mechanism further enhances service ranking accuracy. We conducted extensive experiments, including multi-level sensitivity analysis and comparisons with state-of-the-art methods. The results show that MRM-C outperforms baseline methods by achieving up to a 17% improvement in service selection accuracy and a 12% reduction in failure rates. These findings indicate that the proposed framework substantially improves the performance, stability, and reliability of cloud service provisioning, leading to enhanced user satisfaction.</p>