Artificial intelligence (AI) in healthcare has yielded promising results, aiding in the transformation and adaptation of the healthcare system. AI applications such as machine learning, deep learning, transfer learning, and natural language processing have been employed to strengthen the healthcare system and promote Sustainable Development Goal Three, which focuses on good health for all. Considering AI potentials, this study aims to demonstrate the effectiveness of these techniques in the practical treatment of patients using electronic health data, specifically laboratory blood tests. The study employs many cutting-edge AI techniques to establish a classifier model that is the most suitable for deploying the ideal course of treatment. The study achieves an F1 score of 81% and 70% for the prevalent and non-prevalent situations, respectively, with an overall accuracy of 77% for the random forest classifier. Health data, particularly laboratory test results, are crucial for determining the extent of a patient's health condition. Swift actions are frequently needed for emergency interventions. Using AI applications that mimic human cognitive capacities to enhance diagnosis and personalised therapy is crucial for improving patient well-being and allocating healthcare resources more efficiently. This study reveals that these measures can be feasible and timely in averting health complications that lead to mortality when AI application is incorporated into existing health information systems. As a result, employing AI applications to aid healthcare is indispensable and relevant to strengthening the healthcare system. The findings offer promising results for improving healthcare when incorporated into existing health information systems.