The European industry twin green and digital transition is critical to foster sustainable manufacturing practices that minimize and prevent the environmental impact and resource exhaustion, while ensuring European global competitiveness. AI can play a pivotal role in this transitioning helping to optimize resources, enhancing efficiency, and reducing waste. Nonetheless, AI adoption in the industry is still limited due to the skills gap that hinders the management of these advanced systems. This paper proposes a self-adaptive pipeline for ML with the incorporation of a new Overfitting Index (OI) for self-parameter tuning emphasizing overfitting prevention. The pipeline incorporates self-parameter exploration capabilities exploiting surrogate models to improve computational efficiency. The proposed OI for ML function is evaluated in a well-known regression problem, prior to its real evaluation in an aluminum recycling application to support operators selecting the best combination of scraps. Results indicate that configurations with lower OIs demonstrate superior generalization and robustness, with the surrogate model effectively identifying and refining high OI configurations. The proposed methodology provides a baseline for future development and integration of self-adaptive and self-improving ML solutions in the industry.