Machine learning (ML) has become an essential technology in the development of modern software-intensive systems, particularly in safety-critical domains such as autonomous driving. However, despite the maturity of model-driven and software engineering practices in these domains, the integration of ML components often remains unsystematic and poorly aligned with established engineering workflows. To address this challenge, this paper proposes the Information Meta Model for Machine Learning ( \( IM^3L \) ), a conceptual modeling language that supports the structured design of ML components in complex system contexts. \( IM^3L \) enables engineers to systematically capture and reason about key characteristics of ML-based functionality—including data structure and semantics, class and feature relationships, learning method, and relevant quality metrics—in a way that aligns with established model-driven engineering (MDE) practices. This approach fosters interdisciplinary alignment and establishes a robust foundation for traceability, comparability, and quality assurance within existing model-driving engineering (MDE) practices. To illustrate the practical application of the proposed approach, the paper presents a representative example utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset within a prototypical object detection scenario. The example demonstrates how \( IM^3L \) can be used to systematically document and structure the critical properties and underlying assumptions of an ML-based system. This facilitates a well-grounded understanding of the system’s intended functionality and its integration within the broader system context prior to implementation.