Public health surveillance is crucial for early disease detection, outbreak prediction, and epidemic response. However, traditional surveillance systems primarily rely on structured clinical data, limiting their capacity to capture emerging health threats from diverse and unstructured sources. This study explores the integration of Natural Language Processing (NLP) and Artificial Intelligence (AI) to automate disease surveillance by analyzing unstructured data, including electronic health records (EHRs), social media posts, news reports, and online health forums. Leveraging state-of-the-art NLP techniques—such as transformer-based language models, named entity recognition (NER), sentiment analysis, and topic modeling—an AI-driven surveillance framework is proposed to process, classify, and extract epidemiological insights from vast unstructured text streams in real time. The framework integrates multilingual data processing, anomaly detection, and geospatial trend analysis to enhance early warning capabilities for healthcare authorities. Its effectiveness is evaluated using benchmark datasets, such as the BioCaster Global Health Monitor, and real-world case studies on infectious disease outbreaks, demonstrating significant improvements in detection speed and accuracy. The findings highlight the transformative role of NLP and AI in advancing public health intelligence, improving disease surveillance scalability, and enabling proactive intervention strategies.