Modern healthcare environments generate heterogeneous clinical data spanning symptoms, laboratory reports, radiological images, vitals, and patient history, yet most existing Clinical Decision Support Systems (CDSS) remain confined to structured datasets and lack multimodal integration, multilingual accessibility, and explainability. This paper surveys recent Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) based clinical reasoning systems and proposes an Intelligent Multimodal and Multilingual Clinical Decision Support System that unifies multilingual symptom normalization, Machine Learning based disease screening (XGBoost), Deep Learning based chest X-ray analysis (DenseNet121) and fracture detection (EfficientNet-B0), RAG-based evidence retrieval from PubMed, MedlinePlus and PubMedQA, ICD-10 standardization through the WHO ICD API, and automated PDF report generation. A systematic review of ten recent works reveals a consistent gap: existing systems address either a single modality, a narrow disease category, or lack standardized interoperable ICD output, and none provide integrated multilingual accessibility for low-resource regional languages such as Kannada. The proposed Fusion Engine architecture directly addresses this gap by combining symptom-derived, laboratory, vital-sign, and imaging-derived evidence with retrieval-grounded medical literature into a single explainable diagnostic hypothesis, further supporting specialist recommendation and GeoHealth analytics for regional disease surveillance.

Index Terms— Clinical Decision Support System (CDSS); Retrieval-Augmented Generation (RAG); Large Language Models (LLM); Multimodal Fusion; Multilingual NLP; Explainable AI; ICD Mapping; Chest X-ray Classification; Fracture Detection; GeoHealth Analytics.