Bronchial asthma is a complex respiratory disorder influenced by genetic and environmental factors. Among the genetic factors, the mutated ORMDL3 gene has been associated with an increased susceptibility to asthma. This study explores the potential application of Hidden Markov Modelling (HMM) techniques for identifying bronchial asthma based on the presence of the mutated ORMDL3 gene. HMM is a statistical modelling approach widely used for analysing sequential data with hidden states. In the context of bronchial asthma, HMM can provide a framework to uncover hidden patterns and transitions associated with the mutated ORMDL3 gene, aiding in the identification and classification of asthma cases. This study aims to develop an HMM-based model that integrates genomic data related to the ORMDL3 gene mutation. By considering the hidden states associated with asthma status and incorporating observed variables, such as genetic markers and patient characteristics, the HMM model will identify and classify individuals at risk of bronchial asthma. The proposed HMM model will be trained using available datasets comprising genomic data. The performance of the model will be evaluated using cross-validation techniques to assess its accuracy, sensitivity, and specificity in identifying bronchial asthma cases associated with the mutated ORMDL3 gene.