Background and Objective: Health misinformation disseminated on social media has detrimentally affected the general population’s attitudes toward public health measures, leading to costly and harmful public health crises around the world. Currently, public health officials cannot mitigate these health misinformation trends due to a lack of a comprehensive expert system capable of collecting and analyzing large amounts of social media data. The objective of this study is to design and develop a big data pipeline and ecosystem for the identification and analysis of health misinformation on social media named the Misinformation Analysis System (MAS). Methods: Python, the Twitter V2 API, and the Elastic Stack are the main technologies used in developing the MAS system. The MAS system extracts social media data from the Twitter V2 API using its Data Extraction Framework and applies automatic health misinformation analysis using a pre-trained Latent Dirichlet Allocation (LDA) Topic Model, Sentiment Analyzer, and Information Disorder Identification machine learning model. The analyzed data is then visualized through dashboards and analytics after being loaded into the Elastic Cloud deployment. Results: The system is performing efficiently and accurately. Independent investigators have successfully utilized the system to extract significant insights for a fluoride-related health misinformation use case, spanning a period of 6 years, from 2015 to 2021. The system is currently being used for a vaccine hesitancy use case, spanning a period of 15 years, from 2007 to 2022, and a heat-related illnesses use case (2011 to 2022), respectively. Conclusions: The novel MAS expert system has the potential to help public health officials globally to detect and analyze misleading health information. Moreover, this system can grow to integrate social media data from multiple sources into dashboards for a multiplatform analysis and to support social media data written in non-Western languages.