Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional algorithms rely on well-defined input and output variables, however, there are scenarioswhere the distinction between the input and output variables and the underlying, associated input and output layers of the model, are unknown. Neural Architecture Search (NAS) and Feature Selection have emerged as promising solutions in such scenarios. This paper proposes MICS-EFS, aModel Input-Output Configuration Search with Embedded Feature Selection. The methodology explores internal dependencies in the complete input parameter space for classification task involving both 1D sensor and 2D image data as well. MICS-EFS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate MICS-EFS’s superior performance in comparison to other algorithms, showcasing its effectiveness in model development pipelines and automated machine learning. MICS-EFS achieved significant modelling improvements,underscoring its significant contribution to advancing the state-of-the-art in neural architecture search and feature selection integration.