eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-05-08 19 2 71720 article Gaurav Singh 1 Narander Kumar 1 Shishir Kumar 1 Department of Computer Science, Babasaheb Bhimrao Ambedkar University (A Central University), Vidya Vihar, Raebareli Road, Lucknow, Uttar Pradesh, India A condition where the thyroid gland functions normally but the level of hormones is imbalanced, known as Sick Euthyroid Syndrome. SES is frequently observed in patients suffering from serious infections or chronic conditions and may mimic hypothyroidism. Machine Learning (ML) can assist doctors in diagnosing the disease because of the complex nature of SES. This paper proposed a Stacked RSL model that uses Random Forest, Support Vector Machine, and Logistic Regression ML classifiers with Sequential Forward and Sequential Backward Feature Selection mechanisms to select the best features and an Over-sampling technique to balance the dataset. The proposed model performs better with RandomOver sampling and feature selection methods, achieving 99.55% accuracy using 3,163 patient data taken from the UCI-ML repository. By addressing class imbalance and removing irrelevant attributes, the improved ML model enhances feature quality and model generalization.These technical improvements enable the algorithm to find the necessary diagnostic patterns more effectively than existing approaches, which enhances the overall predictive performance. https://biomedpharmajournal.org/vol19no2/early-detection-of-sick-euthyroid-syndrome-using-stacked-rsl-machine-learning-model/ Data balancing Euthyroid Feature selection Machine learning Stacking classifier