Cucumber (Cucumis sativus L.) is an important horticultural crop that is significantly affected by abiotic stresses such as drought, salinity, waterlogging, and temperature fluctuations. The use of meta-analysis and Correlation-based Feature Selection (CFS) is crucial for identifying key genes, as these methods allow for the integration of diverse datasets and enhance the reliability of findings to pinpoint genes that play significant roles in stress responses. This study conducted an RNA-Seq meta-analysis using 96 stress-exposed samples to identify differentially expressed genes (DEGs) in response to abiotic stresses. The reads were mapped using HISAT2. FeatureCounts was used for quantifying expression, DESeq2 for normalization, and edgeR for identifying differentially expressed genes. The analysis revealed 2,231 DEGs, among which 12 key genes were identified through correlation-based feature selection (CFS). Additionally, validation using the BayesNet model demonstrated an accuracy of 98.96% in identifying DEGs. Codon usage analysis indicated that abiotic stresses influence codon preference patterns; however, no significant linear or nonlinear correlations were found between expression changes and codon usage indices. Overall, the results of this study can help improve breeding-based strategies, including identifying potential targets for developing molecular markers and editing target genes using technologies such as CRISPR-Cas9, to enhance plant resistance to abiotic stresses.