<p>This study investigates the optimization of population mean estimation in stratified random sampling under fixed budget constraints. Although stratified sampling improves precision by dividing the population into homogeneous strata, data collection costs often vary across strata, making optimal resource allocation essential. We develop a cost-efficient allocation strategy that minimizes the variance of the estimated population mean under a linear budget constraint. The methodology incorporates auxiliary information and cost variations across strata to determine optimal sample sizes. Both real data and simulated data are used to evaluate the performance of the proposed allocation strategy. Analytical results, supported by simulation experiments, demonstrate that optimal allocation substantially improves estimation efficiency when compared with traditional proportional and equal allocation methods. Findings from the real dataset further confirm the practical applicability of the method in actual survey settings. Overall, the study provides valuable guidance for researchers and survey practitioners seeking to balance statistical accuracy with financial limitations in diverse real-world applications.</p>