The rapid growth of digital information has led to the generation of large amounts of unstructured data in formats such as text documents, images, and PDF files. Since this type of data does not follow a fixed structure, extracting useful information from it remains a significant challenge for conventional data processing systems. This paper presents a unified framework for transforming unstructured content into structured and machine-readable data. The proposed approach combines Natural Language Processing (NLP) and Optical Character Recognition (OCR) techniques to handle information from textual documents, scanned records, images, and PDF files. NLP methods are employed to identify important entities, keywords, and contextual information from textual content, while OCR technology is used to extract text embedded within images and document files. The extracted information undergoes preprocessing, cleaning, and normalization before being organized into structured formats such as JSON and CSV. The developed framework improves data accessibility, simplifies information management, and reduces the effort required for manual data extraction. The solution can support various applications, including document management systems, business intelligence, healthcare record processing, and information retrieval platforms. Experimental evaluation indicates that the framework provides an efficient and reliable method for converting heterogeneous unstructured data into structured datasets suitable for analysis and decision-making.