Hyper-realistic world visualizations become the runners of modern visualization which plays a major role in informing people whether a phenomenon actually exists or not. Despite the AI's pre-trained models, which have been very successful in a lot of areas, for example, image and language processing, the disorientation of AI decision-making is present. Explainable AI (XAI) is a methodology that investigates the reasons behind AI models’ decisions with the aid of visualization techniques and algorithms. XAI could act as an intermediary in the healthcare sector, thus increasing trust, transparency, and accountability of AI-based systems in the future. Using XAI to analyze health data can be one of the ways to ensure models are working as designed, revealing the hidden patterns they find and transfer the information for continuous learning, and enable the users to trust the information they get. On the specific subject of OHA, for example, the development of RU-LI-AD code, hazel would enable the project's capacity to accomplish advanced anal sis. Parents will have instruments available that will assist them with locating their children by accessing their profiles of visual and behavioral characteristics. Indeed, social fields present many innovative yet effective works; this includes the development of virtual representations of audio-visual-textual information which even may be close to offering almost a 1:1 auditory-visual experience Enhanced intelligibly makes identification of prejudices and mistakes in AI models by permitting constant cleaning and betterment. On a related note, increased knowledge of AI models and their results is one of the contributors to the establishment of transparent and responsible health care practices through XAI. Moreover, with the help of XAI, users will be able to see the hierarchy of AI predictions, thus making it possible to get information on the findings of the different factors that affect the outcome.