Sorghum is a widely cultivated crop in arid and semi-arid regions, particularly in sub-Saharan African countries. Several factors have been influencing sorghum production, with genotype and genotype-by-environment interaction being the primary sources of variability in yield and yield-related traits. This dissertation aimed to identify the best statistical approach for sorghum genotype selection with high performance in grain yield and yield-related traits under non-irrigated and irrigated conditions. Specifically, the sorghum genotype selection with superior performance in grain yield under both conditions is done by treating the genotypes as fixed or random effects using parametric, nonparametric statistical models and unsupervised machine learning to perform the genotype selection with superior performance in major traits. The experiment used water availability as a treatment, and each replication within the treatment levels used a lattice square design for data collection. A design consisted of 14×14 square experimental units containing 196 genotypes, where each row of the square represented a block receiving 14 genotypes, from which phenotypic traits were measured for the analysis. Findings revealed that the sorghum genotypes with superior performance in grain yield employed different parametric models. A result of genotype selection using mean performance in analysis of variance, with an assumption of genotypes treated as fixed effects, which is less important compared to other parametric models considering the genotypes as random effects. The genotype selection with superior performance in grain yield using mixed-effects models was a more reliable selection compared with the classical model. Findings of genotype selection with superior performance in major traits using a distributional assumption-free statistical model were advisable to scan the genotypes performing best in various phenotypic traits. Classification of phenotypic traits and sorghum genotypes was implemented to investigate high-performing genotypes in major phenotypic traits. The outcomes of this study can help academics and researchers in plant breeding for sorghum genotype selection in phenotype traits using advanced models. This can serve as an alternative statistical analysis approach for plant breeders.