The approach used to identify inbred lines that can produce superior hybrids is costly and time-consuming. It requires creation of all possible crosses and evaluation of the crosses to estimate combining abilities for the desired traits. Predicting heterosis or hybrid performance in any way possible may help to reduce the number of crosses to be made and evaluated. In this study, four sets of experiments were conducted to determine whether heterosis can be predicted based on inbred line performance, genetic distance between parents and genomic prediction model. The first experiment was aimed at assessing the levels of genetic diversity, population structure and linkage disequilibrium (LD) in 279 public sorghum inbred lines, based on 66,265 SNPs generated using the genotyping-by-sequencing (GBS) platform. The inbred lines were developed at different times over the last two decades and harbor robust diversity in pedigree and agronomic characteristics. Some of the inbreds are resistant to Acetolactate synthase (ALS) and Acetyl co-enzyme-A carboxylase (ACC) inhibitor herbicides. The mean polymorphic information content (PIC) and gene diversity across the entire inbreds were 0.35 and 0.46, respectively with non-herbicide resistant inbreds harboring more diversity than the herbicide resistant ones. The population structure analysis clustered the inbred lines into three major subgroups according to pedigree and fertility-reaction with the maintainer lines (B-lines) distinctly forming a separate cluster. Analysis of molecular variance (AMOVA) revealed more variation within subgroups than among subgroups. Substantial linkage disequilibrium (LD) was detected between the markers in the population with marked variation between chromosomes. This information may facilitate the use of the inbreds in sorghum breeding programs and provide perspectives for optimizing marker density for gene mapping and marker-assisted breeding. The second experiment, based on 102 F1 hybrids developed by intercrossing closely and distantly related inbreds, was conducted to investigate the relationship of genetic distance between parents with hybrid vigor or heterosis. The F1 hybrids alongside their parents were evaluated at two environments in a randomized complete block design with three replications. The results show that correlations of genetic distance between parents with hybrid performance and heterosis were variable and dependent on the trait. Though most were statistically non-significant and not strong to be used as predictor for heterosis, the results tend to show that certain level of genetic distance between parents is needed to capture maximum heterosis and hybrid performance. The objective of the third research study was to determine whether traits measured on parents can be used to predict hybrid performance in sorghum and to assess the combining ability of selected inbreds. Forty-six parental inbred lines and 75 F1 hybrids generated from intercrossing the inbreds were evaluated in four environments in a randomized complete block design with three replications. The average performance of the parents (mid-parent) was significantly correlated with hybrid performance for thousand kernel weight, days to flowering and plant height. Significant general (GCA) and specific (SCA) combining abilities were observed for most traits, with highly significant GCA effects observed for most traits as compared to SCA indicating that additive genetic effects are more important in affecting the inheritance of the traits measured. Results show that studying parental inbred line performance could generate important information for predicting hybrid performance in sorghum. The fourth experiment was aimed at assessing the efficacy of genomic prediction of hybrid performance in sorghum. Genomic prediction was performed with five-fold cross-validation procedure on 204 F1 hybrids developed using 102 inbred lines. A total of 66,265 SNP markers generated using genotyping-by-sequencing were used in this study. Results showed that increasing training population size increased prediction accuracies for all traits with the effect being different for different traits. Also, considering additive effects alone versus additive and dominance effects in the model showed similar trend of prediction accuracy but the full model (considering both additive and dominance effects of the markers) provided better prediction at least for some of the traits. The results suggest that genomic prediction could become an effective tool for predicting the performance of untested sorghum hybrids thus adding efficiency to hybrid selection.