The objective of the machine learning algorithms project for Disease Gene Association Classification is to devise a computerized method to recognize and group genes linked with diseases by employing machine learning techniques. The project entails scrutinizing extensive genomic data to pinpoint genetic variations that are correlated with specific ailments. The proposed method will employ various machine learning algorithms such as AdaBoost, XGBoost, and ANN to classify genes into two categories, disease-associated or non-disease-associated, based on their genetic characteristics. The outcomes will be displayed visually, and researchers can investigate the identified gene-disease connections. The ultimate objective of this endeavor is to furnish a precious asset for the medical and exploration circles, streamlining the effective and precise detection of genes linked with a wide range of ailments. This resource will be crucial in the development of personalized medicine and targeted therapeutic interventions for different ailments.