Modern medical research depends on Genomic Data (GD) analysis to study genetic elements which cause diseases. The development of sequencing technologies has generated enormous datasets which create substantial obstacles for conventional bioinformatics analysis methods. Artificial Intelligence (AI) provides effective solutions to manage complex high-dimensional genomic data. The research implements Support Vector Machines (SVM), Random Forest (RF) and Deep Neural Networks (DNNs) as AI methods to forecast disease results from genomic data. The research assesses how these algorithms perform for disease prediction while addressing two main obstacles which include model interpretability and data privacy. The experimental results show that Deep Neural Networks achieve superior accuracy and prediction power than other methods but their high computational requirements and unclear interpretation make them less suitable.