Artificial Intelligence has made it easy to create realistic images, videos, and texts. These technologies have been misused to create deepfakes and online scams, which can lead to the spread of misinformation, financial scams, and cybersecurity attacks. It is hard to detect such content by human beings, as it is time-consuming. Hence, there is a need to develop an automated detection system for AI-generated content. The proposed project aims to develop a multimodal AI-generated content detection system that can analyze images, videos, and texts to detect potentially fake or scam content. The system can detect deepfakes in images and videos using a Convolutional Neural Network (CNN) model, and it can also analyze the text messages sent by the user to detect scams using a machine learning-based approach. The application has been developed as a web application using the Flask framework in Python. This processed media is analyzed, and the important features are identified, providing a probability score on whether the media is real or fake. The output is given in percentage probability, making it easier for the user to interpret the results. All analysis results are stored in a SQLite database, which is used for monitoring and administrative purposes. This proposed system has shown how deep learning and machine learning can be combined into a single framework to detect AI-generated content. This type of system can be used to enhance digital security, helping users identify fake media and possibly scam messages.