Currently, the healthcare engineering has benefited from the computational tools and techniques in its development completely changing the diagnosis, treatment, and management of diseases. This paper offers an intense analysis of a myriad of facets of application of computational tools and approaches in healthcare engineering. These tools and techniques are used in many fields such as in medical application, image processing, simulation, biotechnology, and in pharmacogenomics. Applications that include computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound have benefited from computational algorithms. These algorithms are used in reconstructing images, segmenting them and analyzing them, and these allow clinicians to get insights from composite medical information. In addition, sophisticated approaches to predictive modeling have developed great proficiency in disease prediction, prognosis, and risk stratification using machine learning and deep learning. These models incorporate a huge amount of biomedical knowledge to identify patterns, biomarkers, and genetics associated with diseases for early diagnoses and subsequent acceptable interventions. In addition, bioinformatics uses computational and statistical approaches to analyze biological information, the examples of which include the genomic, proteomic, and metabolomic information. It enables the identification of those molecular mechanisms which lead to diseases and assist in the generation of disease prevention and treatment strategies. Further, use of “virtual simulations and modeling techniques” facilitates replication of various physiological actions and reactions, drug effects, and medical devices. These in turn assist in designing and enhancing of treatment procedures as well as medical equipment. Secondly, data integration and interoperability frameworks facilitate interchange and integration of healthcare data from one system to another, to enhance the interoperability to advance a collaborative, decision-supportive, and pliant healthcare. Furthermore, the context as well as applicability of the computational tools and technique needs to address the critical issues of privacy, security, and algorithmic fairness in healthcare engineering. This calls for implantation of effective laws and regulation as well as set ethical standard that need to be observed in the course of offering care to the patient.