This research paper presents a comparative analysis of Privacy-Preserving Machine Learning (PPML) techniques tailored to the unique landscape of Indian healthcare data. The rapid digitalization of healthcare in India, coupled with the imperative to protect sensitive patient information, underscores the need for effective PPML solutions. The primary objective of this study is to evaluate and compare the performance, resource utilization, privacy guarantees, and practicality of three prominent PPML techniques: Federated Learning (FL), Differential Privacy (DP), and Secure Multiparty Computation (SMC), when applied to Indian Healthcare Data (IHD). The research methodology involves the collection of diverse healthcare data from the National Health Portal of India (NHPI). Data preprocessing, anonymization, and feature engineering are performed to ensure data quality and privacy preservation. Data analysis is conducted using Python with scikit-learn and PyTorch libraries, encompassing descriptive statistics, inferential statistics, and machine learning algorithms. The implications of this study are far-reaching. Healthcare providers in India can make informed decisions about the adoption of PPML techniques, leading to enhanced patient care and operational efficiency. Policymakers can use these findings to develop data privacy regulations tailored to the Indian healthcare context. The global relevance of this research extends to countries facing similar healthcare challenges, contributing to international best practices. Furthermore, this study stimulates further research in the field of PPML, encouraging the development of more efficient techniques and tools for privacy-preserving healthcare applications. Differential Privacy and Secure Multiparty Computation also performed well, offering different trade-offs between privacy and utility. Federated Learning and Secure Multiparty Computation (FLSMC) showed promising results in terms of resource efficiency, privacy guarantees, and adaptability to healthcare data settings. It enables computations on encrypted data, allowing data to remain confidential throughout the entire analysis pipeline. Moreover, AI's remarkable capacity to analyze data and make complex analyses amplifies privacy concerns. The technology's potential to infer sensitive information, such as a person's location, preferences, and habits, poses risks of unauthorized data dissemination.