The broad category of pulmonary conditions known as interstitial lung disease (ILD), which is marked by fibrosis and inflammation, makes early diagnosis and categorization extremely difficult. Deep Learning (DL) and Machine Learning (ML), two recent developments in artificial intelligence (AI), have demonstrated impressive promise in improving ILD detection, segmentation, and prognosis. The performance of state-of-the-art techniques in ILD pattern recognition and clinical relevance are highlighted in this survey paper, which includes a thorough analysis of convolutional neural networks (CNNs), Vision Transformers, U-Net variants, hybrid radiomics approaches, and semi-supervised segmentation frameworks. Preprocessing, segmentation, and classification—three crucial phases in ILD image analysis—are rigorously analyzed using methods such Contrast Limited Adaptive Histogram Equalization (CLAHE), adaptive filtering, Fuzzy C-Means clustering, and feature extraction discussed in detail. The study also examines issues such the lack of annotated datasets, variations in lung anatomy, acquisition problems, and the requirement for models that are both clinically deployable and explicable. The survey also indicates research shortcomings, such as inadequate large-scale validation, integration of multimodal clinical data, and understudied forms of ILD following COVID-19. This review offers a thorough viewpoint on AI-driven ILD analysis by combining recent research, highlighting the potential for reliable, comprehensible, and scalable solutions for precise diagnosis, prognosis, and early intervention in clinical practice.