The article presents a machine learning approach to address concerns about the dissemination of unfriendly content through memes. Specifically, it focuses on detecting unfriendly memes for content moderation purposes. It will compare three different methods which respectively focusing on textual content, image content and the combination of text and image content. The approach utilizes advanced machine learning models to distinguish between friendly and unfriendly content by leveraging both textual and visual elements of memes. State-of-the-art techniques such as Optical Character Recognition (OCR) for text extraction and Convolutional Neural Networks (CNNs) for image analysis are employed. Additionally, recurrent neural networks (RNN), gated recurrent units (GRU), and long short-term memory (LSTM) models are utilized for text classification. MLP is used in the classification process of combination. The models' performance is evaluated using objective measurements such as accuracy, ROC curves, and confusion matrices. The results demonstrate the effectiveness of the proposed approach in identifying unfriendly content and its implications for content detection on social media platforms.