This paper draws attention to the Ayushman Bharat Health Account (ABHA), which constitutes a pivotal element of the Ayushman Bharat Digital Mission (ABDM) implemented in India. The ABHA database functions as a centralised repository of health-related information, offering a platform for IEC initiatives aimed at increasing awareness, encouraging healthy behaviours, and disseminating public health messaging. The integration of Information, Education, and Communication (IEC) and Artificial Intelligence (AI) technologies has the potential to augment patient education and engagement, streamline disease prevention initiatives, and enhance health literacy. Centralized health data can help in promoting telehealth by providing health care providers with access to patient medical records, enabling them to provide remote consultations and treatments. Exploring the function of Ayushman Bharat Health Account (ABHA) in telehealth for sustainable development necessitates an interdisciplinary view of healthcare delivery in India. Telehealth, which is the use of technology to provide remote healthcare services, can increase the reach and efficacy of ABHA. The adoption and success of ABHA and telehealth in India are influenced by numerous social, economic, technological, and policy-related factors, which can be identified through an interdisciplinary research approach. Such research can inform the development of a sustainable healthcare system that capitalizes on the strengths of ABHA and telehealth to enhance the population's health and well-being in India. This paper proposes the implementation of ABHA, IEC, AI, and telehealth in India, which have the potential to transform healthcare delivery by augmenting patient care, strengthening disease prevention efforts, and encouraging healthy lifestyles. This approach can potentially save lives and reduce healthcare costs in the long run. However, the implementation of such a system may raise concerns over privacy and data security, especially in a country with a large population and limited resources for enforcing regulations and protecting sensitive information.

Research in brain stroke prediction is very crucial as it can lead to the development of early detection techniques and interventions that can enhance the prognosis for stroke victims. Early detection and intervention can help to minimise the damage caused by a stroke, reduce the risk of long-term complications, and enhance the general quality of life for people who have survived a stroke. Additionally, research in stroke prediction can help to identify risk factors and improve understanding of the underlying causes of stroke, which can lead to the development of better prevention strategies. Research on brain stroke prediction is ongoing and has led to the development of various models and tools for predicting the risk of stroke and detecting it early. However, the implementation and use of these tools in clinical practise vary depending on several factors, such as the availability of resources, the specific healthcare system, and the level of awareness and acceptance of these tools among healthcare providers and patients. In general, risk prediction models may be used to quickly identify individuals at high risk of stroke and target them for preventive interventions, such as lifestyle changes, medication management, and screenings. Early detection tools can be used to quickly identify stroke symptoms and initiate appropriate treatment, which can improve outcomes for stroke patients. However, it is important to note that research and development of these models and tools are ongoing, and their use in clinical practise is constantly evaluated and updated. It may take time for these tools to be widely adopted in clinical practise and to see their real-world impact. This research paper focuses on predicting brain stroke occurrence using a range of machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Bernoulli Naive Bayes (BNB), and a Voting Classifier. The main emphasis of this research was to compare the effectiveness of oversampling techniques, as the data was significantly imbalanced. We have used evaluation metrics in this research to assess the accuracy, precision, recall, and other key performance indicators of a model's predictions. Area under the Receiver Operating Characteristics Curve (AUC), minority class accuracy, and majority class accuracy were used to assess the approaches.