Interactive Data Mining with Machine Learning (IDM-ML) embodies a collaborative paradigm where users actively participate in the data-driven decision-making process. This paper explores user-centric approaches and tools in IDM-ML, focusing on enhancing accessibility, interpretability, and adaptability of machine learning models. The methodology emphasizes user involvement from problem formulation to model interpretation, ensuring alignment with diverse user backgrounds. Intuitive interfaces and graphical tools facilitate user-friendly interactions, enabling domain experts to contribute meaningfully. Key aspects include iterative feedback loops, transparent model interpretability, and continuous adaptation to evolving user requirements. The paper presents an overview of tools designed for user-centric IDM-ML, emphasizing their role in bridging the gap between machine learning experts and end-users. The user-centric design philosophy contributes to the democratization of machine learning, making it a practical and valuable tool in real-world scenarios.