In this work, we have performed comparative evolutionary analysis, functional genomics analysis, and machine learning analysis to identify the molecular factors that discriminate between multipartite and unipartite bacteria, with the goal to decipher taxon-specific factors and those that are prevalent across the taxa underlying the these traits. We assessed the roles of evolutionary mechanisms, namely, horizontal gene transfer and gene gain, in driving the divergence of bacteria with single and multiple chromosomes. In addition, we performed functional genomic analysis to garner support for our findings from comparative evolutionary analysis. We found genes such as those encoding conserved hypothetical protein DR_A0179 and hypothetical protein DR_A0109 in Deinococcus radiodurans R1, and Putative phage phi-C31 gp36 major capsid-like protein and hypothetical protein RSP_3729 in Rhodobacter sphaeroides 2.4.1, which are located on accessory chromosomes in both bacteria and were not found in the inferred ancestral sequences, and on the primary chromosomes, as well as were not found in their closest relatives with single chromosome within the same clade. These genes emphasize the important potential roles of the secondary chromosomes in helping multipartite bacteria to adapt to specialized environments or conditions. In addition, we applied machine learning algorithms to predict multipartite genomes based on gene content of multipartite genomes and their unipartite relatives, and leveraged this to identify genes that are deemed important by machine learning in discriminating between multipartite and unipartite genomes. This approach led to the identification of marker genes that could be used in discriminating between bacteria with multipartite genomes and. bacteria with single chromosome genomes Furthermore, we examined modules in gene co-expression networks of multipartite Rhodobacter sphaeroides 2.4.1 and its close unipartite relative Rhodobacter capsulatus SB 1003 that were enriched in genes differentially expressing under stressful conditions representing different experiments. This led to the identification of 6 modules in the Rhodobacter sphaeroides 2.4.1 network and 3 modules in the Rhodobacter capsulatus SB 1003 network, which were significantly enriched (2-fold or more) in differentially expressing genes, revealing the vital roles of these gene modules representing different pathways or networks of pathways (known or unknown) in enabling the bacteria to adapt to stressful conditions. Overall, our study highlights genetic factors that may be driving the evolution of multipartite bacterial genomes; future studies may focus on unraveling the specific roles of these genes in the adaptation and maintenance of multipartite genomes.