Edge assisted Experiential Computing has emerged as a pivotal strategy in the Universal healthcare sector, aiming to enhance Client performance by providing immersive and memorable experiences. However, existing methods lack comprehensive measurement tools and real-time feedback mechanisms to gauge effectiveness accurately. Traditional approaches often rely on subjective assessments and delayed feedback, hindering the timely identification of Client preferences and sentiments. Additionally, the absence of data-driven insights limits the adaptability of Computing strategies to evolving consumer requirements. To address these limitations, this study proposes an innovative approach: an Artificial Intelligence-based Edge assisted Experiential Computing System (AI-EMS). By leveraging AI for real-time data analysis and predictive modelling, AI-EMS offers personalized experiences tailored to individual preferences, thus enhancing Client performance and loyalty. The proposed AI-EMS integrates seamlessly into existing Computing frameworks, providing actionable insights to optimize resource allocation and improve Client engagement. Through its adaptive nature, AI-EMS enables businesses in the Universal healthcare sector to stay ahead of market trends and consistently deliver exceptional experiences. Findings from this study reveal that AI-EMS significantly enhances Client performance levels by accurately anticipating their preferences, optimizing service delivery, and fostering deeper emotional connections with the brand. Moreover, the implementation of AI-EMS leads to improved operational efficiency and higher returns on Computing investments, underscoring its potential as a transformative tool in the healthcare industry.