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MongoDB is increasingly adopted for artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) applications due to its flexible, document-oriented architecture and scalability. However, the challenge of efficiently managing vast, heterogeneous, and unstructured data generated by modern IoT and AI/ML systems remains underexplored. This paper examines how MongoDB addresses these challenges and evaluates its effectiveness compared to traditional relational databases. Our approach involves analyzing MongoDB’s schema-less design, native support for diverse data types, and horizontal scaling via sharding, with a focus on real-time analytics, integration with AI frameworks, and IoT-specific features such as time-series collections and change streams. Performance metrics, including data ingestion rates, query latency, and scalability—are assessed through case studies and benchmarking against MySQL. Results indicate that MongoDB outperforms relational databases in flexibility, ease of handling unstructured data, and scalability, particularly in scenarios involving large-scale sensor data and dynamic AI/ML workflows. For example, in IoT deployments, MongoDB supports real-time analytics and efficient storage of billions of records, enabling rapid insights and predictive maintenance. In conclusion, MongoDB’s architecture and evolving feature set make it a robust platform for organizations leveraging AI, ML, and IoT. Its ability to manage complex, high-velocity data streams enhances operational efficiency and supports advanced analytics, positioning it as a preferred solution for next-generation data-driven applications