This article is an advanced AI-based system designed to enhance patient comprehension of pathology reports. The technology simplifies the extraction of important information from PDF-based reports by avoiding potentially confusing manual review processes. The core purpose of the system is to deliver health information to patients in a timely and easily comprehensible manner, bridging the communication gap between medical professionals and patients. The system does this by generating age- and gender-specific summaries. This helps to decrease patient anxiety and uncertainty while making for more productive discussions about treatment options and preventive measures. This technology also helps to improve communication between patients and doctors, thereby enabling more productive discussions during consultation hours. Patients can be able to participate actively in conversations that will have them understanding their health status better and hence make informed decisions and get streamlined healthcare. The system uses advanced natural language processing and machine learning algorithms to identify and extract relevant data from pathology reports, including test results, reference ranges, and potential health implications. For this purpose, OCR and computer vision techniques were initially tested; however, due to their limited accuracy in handling complex medical terminology, a large language model (LLM) was adopted, which showed superior performance with an accuracy of 92.3%. This empowers patients to work actively with clinicians in the management and control of health.