Ensuring accessible and affordable healthcare is a global priority. This is not only a moral and social responsibility but also a necessary ingredient for the sustainable long-term development of our economy and society. Access to doctors is often limited, making medical consultations costly and time-consuming. These challenges make it difficult for many people, especially in middle and low-income countries, to receive timely and proper care. This paper presents an intelligent healthcare chatbot “AI-care” designed to assist patients by answering medical queries, scheduling doctor appointments, and providing preliminary diagnoses. We leveraged various datasets, machine learning, deep learning models, and natural language processing techniques to build an efficient chatbot. A sequence-to-sequence model with an attention mechanism employed for user question answering task, which achieved a validation accuracy of 97%. A neural network for appointment scheduling. For disease prediction we trained decision tree model to classify general illnesses (96.04% accuracy), an ensemble model for heart disease (98.52%), Gradient Boosting for diabetes (98.2%), and an SVM model for breast (94.9%) and cervical cancer (93.8%). With an average accuracy of 96%, the chatbot demonstrates promising results in healthcare assistance. However, it does not replace medical professionals but offers preliminary insights before formal consultation. If a critical condition is detected, the chatbot recommends seeking professional medical advice. Additionally, it ensures user privacy by avoiding reinforcement learning and not storing conversation logs. These findings highlight the potential of AI-driven chatbots in improving healthcare accessibility, offering a scalable solution to bridge the gap between patients and medical services.