The evolving capabilities of AI models such as GPT -3 and GPT -4, and Deepseek have rendered it ever more difficult to distinguish between human-written text and AI-generated text. The issue raises serious concerns regarding plagiarism, disinformation, and authorship of electronic content in academic, journalistic, and educational spheres. For this aim, an innovative tool – AI vs Human Text Detector is introduced, it is a web application created to ascertain whether information was authored by a human or by an artificial intelligence model. The system uses Natural Language Processing (NLP) methods to examine prominent linguistic characteristics like perplexity and burstiness that distinguish the homogeneous pattern of AI text from the naturally heterogeneous patterns of human writing. The pre-trained GPT -2 is utilized to quantify textual predictability and variability, thus improving classification accuracy. Visualization of results is facilitated through Matplotlib and Plotly. The software tool is optimized to run on low-cost, commonly available resources, ensuring both accessibility and scalability. The proposed model achieves a high accuracy of 99.2% in detecting AI-generated content, outperforming existing models like ERT, CNN, and BERT-CNN. Future enhancements involve multilingual detection, hybrid detection models. The contribution of this work lies in supporting content integrity and ethical AI use in digital communication.