In this paper, Big Data Processing was utilized to develop a Predictive Analytics Model with the goal of determining the risk for an individual manifesting osteoporosis in later life. The analyzed dataset consists of the genomic information of over 2,500 individuals from all around the world. This model development leverages the novel genetic pleiotropic information, the two or more apparently unrelated phenotypes caused by a single gene, which was the results of the precedent studies. The dataset was examined for the mutations associated with osteoporosis and cardiovascular disease from the population genetics perspectives. The study proposes the automatic histogram clustering as an effective and intuitive visualization method for high dimensional dataset. The data visualization and auto-clustering results revealed a significant correlation between a person's regional background and the frequency of occurrence of the 35 single nucleotide polymorphisms (SNPs). These 35 SNPs are associated with osteoporosis and/or cardiovascular disease (CVD). Distinct SNP frequency of occurrence profiles were observed for specific geographic regions. Machine learning algorithms such as Logistic Regression, Random Forest, and AdaBoost were then applied to predict the occurrence of 7 osteoporosis-related-SNPs based on the existing CVD-related-SNPs input as an experiment. The model's validity was confirmed by a separate result utilizing a set of data obtained through Affymetrix microarray mRNA expression signal values for the specific SNP(s) in individuals with and without osteoporosis. Furthermore, these results confirmed the genetic linkage between osteoporosis and Cardiovascular related parameters such as High Density Lipoprotein (HDL) and Systolic Blood Pressure (SBP).