Sana Fathima, Dr. Mohammad Pasha, Samreen Sultana 02 08 The health and wellbeing of people are enormously impacted by sleep problems including insomnia sleep apnoea and other illnesses The quality of life for those who are impacted can be improved by early diagnosis and successful treatment made possible by an accurate and effective classification of various conditions For categorisation the current systems mostly use Artificial Neural Networks ANN which are efficient but sometimes computationally demanding and difficult to understand In order to categorise sleep disorders this study suggests a Random Forestbased method using a dataset of 400 samples with 13 pertinent variables The Random Forest model was chosen because it is robust easy to understand and has a higher capacity to manage intricate nonlinear interactions in the data The study uses this algorithm to categorise sleep disorders into three groups sleep apnoea insomnia and none Its performance is better than that of conventional ANNbased systems Standard performance criteria such as accuracy precision recall and F1score are used to evaluate the Random Forest model The results demonstrate that the suggested method performs better than current models providing improved accuracy and dependability in the categorisation of sleep disorders Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026