The integration of Artificial Intelligence (AI) in radiology, especially for chest computed tomography (CT) scan analysis, marked a significant advancement in medical diagnostics, aiming to improve patient care and streamline the workflow for radiologists. This review article examined the role of current AI technologies, including machine learning (ML), deep learning (DL), convolutional neural networks (CNN), and radiomics, in enhancing the detection and characterisation of lung diseases. These technologies are instrumental in identifying complex patterns within imaging data and constructing more informed decisions regarding disease severity, progression, and potential treatment options. Deep learning and CNN have demonstrated effectiveness in analysing the intricate details present in chest CT scans, offering a high degree of accuracy. Radiomics complements these methods by extracting quantitative features from medical images, providing deeper insights into disease characteristics that are not visible through standard imaging techniques. The application of AI has shown promise in improving the diagnosis and management of interstitial lung diseases and lung cancers, contributing to the development of personalised treatment plans. However, this review also highlights limitations, such as small sample sizes in studies, which may impact the generalisability of AI applications in this field. Despite these challenges, the ongoing incorporation of AI into radiological practices is anticipated to significantly enhance the accuracy and efficiency of lung disease diagnostics, setting a foundation for future research and improvements in clinical practice.