Due to their complex presentations and the limitations of traditional diagnostic methods, rare genetic disorders have always been among the most difficult to diagnose. Many conditions remain undocumented for several years, which has led to delays in both treatment and interventions. The increase in multi-omics data, including but not limited to genomics, proteomics, metabolomics, and transcriptomics, opens up new avenues in regards to these challenges by providing a wide look into the biological systems of an individual. Adding several omics layers together increases the possibility of going towards an accurate diagnosis; the problem is that this is a limiting factor for the effective use of such complexity. ML now promises a way out from this complexity. This is made possible by the use of ML capability: processing big, multi-dimensional data sets to find patterns and correlations that might otherwise have been missed. Recent breakthroughs in ML, including deep learning and transfer learning, also reflect their potential for integrating multi-omics data and improving early diagnosis for a rare genetic disorder. Still, this direction has been poorly represented by research papers, at least with respect to the use of ML in diagnosing a rare disease. This research will work on formulating an ML framework with the capability to integrate multiomics data for the prediction of rare genetic disorders. The hope here is that, through availing the full capacity of ML in the management of complex interactions among data, this research may be useful in the improvement of early diagnosis and treatment of these conditions. Beyond that, the research hopes to enrich the emerging sciences of personalized medicine for future applications of ML to diagnostics of rare diseases and beyond.