AI has the potential to greatly transform the field of healthcare management through improving the accuracy of diagnoses, effectiveness of treatments, and overall experience of patients. This paper aims at reviewing the utilization of four algorithms in the healthcare sector namely; Decision Trees, Support Vector Machines, Random Forests, and Neural Networks. These algorithms are assessed on the basis of performance indicators like accuracy, precision, recall and F1 score using the patient record dataset of size 20,000 in the present work. Performance seems to show that Neural Networks have the highest accuracy of 93 over the other algorithms. 5%, precision of 92. 0%, recall of 93. 0 %, Precisions 92 %, Recal 93 % and F1 score of 92. 5%. This is followed by Random Forests with a 91. 3%, precision of 90. 0%, recall of 90. , accuracy of 5%, and F1 score of 90. 2%. The research findings show that SVM yields to 89 percent accuracy. 0%, precision of 87. 5%, recall of 88. 0%, Precision of 0.88 and F1 score of 0.874. for Decision Trees falls 85 % and for Naïve Bayes it falls up to 73% whereas for Random Forest the accuracy comes near to about 7%. 2%, precision of 82. 5%, recall of 84. The accuracy for the producers is 0%, recall is 0%, and F1 score is 83. 2%. The comparative analysis with the related works explores the pace and progress of AI in various areas of the healthcare sector’s concerns. Among the identified findings they include proactive utilization of Artificial Intelligence in enhancing the delivery of health care, use of Artificial Intelligence in achieving patients’ better-quality health care, and apply of Artificial Intelligence in the decision making process. The future area is focused on the improvement of the algorithm of AI, the ethical and legal questions, and the usage of AI in the solving of the worldwide health issues