Organizations across sectors are increasingly digitizing operations to improve agility, transparency, and decision quality. Low-code enterprise application platforms have emerged as a key enabler of this transformation by allowing rapid development of business applications with minimal hand coding, lowering barriers between domain experts and software delivery teams. However, many low-code applications remain workflow-centric, relying on static rules and predefined logic that struggle to adapt to data variability, operational uncertainty, and evolving business environments. At the same time, machine learning techniques have matured into practical tools for prediction, classification, and optimization, yet their integration into enterprise applications often remains complex, fragmented, and developer-intensive. This paper examines the embedding of machine learning decision logic into low-code enterprise applications as a pathway to augment automation with adaptive intelligence. From a broad architectural perspective, it analyzes how data pipelines, model inference services, and governance mechanisms can be aligned with low-code design paradigms. The study then narrows its focus to practical integration patterns, including model encapsulation as reusable components, event-driven inference, and human-in-the-loop decision support. Key challenges related to explainability, lifecycle management, security, and scalability are discussed. By framing machine learning as a modular decision layer, the paper highlights intelligent, maintainable low-code applications for enterprises.