<p>Multi-cloud development improves scalability, dependability, and flexibility by integrating distributed resources across various cloud platforms. It reduces vendor lock-in, improves performance, and enables more cost-effective resource allocation. It additionally provides redundancy, providing data availability and tolerance to interruptions, hence improving total system robustness and flexibility. In this research, we aimto develop an innovative model for optimizing data service in multi-cloud driven storage platforms. We propose a novel progressive squirrel search optimization (PSSO) method for cost-effective multi-cloud data management with excellent dependability. Initially, we delineate the optimization with a multi-objective issue concerning the storage of data in multi-cloud computing. Then, the PSSO is utilized to handle data hosting optimization issues in multi-cloud systems. Our proposed model gradually reduces mutation rates to facilitate exploitation around promising solutions, optimizing multi-cloud data storage cost-effectively while maintaining dependability. PSSO stimulates diversity by distributing rates and expanding the search discipline. We implement storage mode modification with variable data access frequency (DAF). DAF storage modes with various values for the parameters. At DAF values between 0.2 and 1.0, the corresponding storage modes (m, n) changed. For instance, at DAF 0.2, the storage modes were 7 and 9, whereas at 1.0, they were 2 and 3. Combinations like (3, 4) and (2, 3) were among the storage modes (m, n) that concentrated on DAF values between 0.31 and 0.40. Across different DAF levels, this analysis aids in determining the best configurations for effective storage management. Extensive experiments have been conducted to further examination of the suggested method’s efficacy. We compare our proposed method with other conventional models to evaluate the effectiveness of the proposed model.</p>