The project Spam Classification and Hate Speech Detection aims to build intelligent machine learning models capable of automatically identifying spam messages and hate speech content from text data. With the increasing volume of online communication, detecting unwanted or harmful messages has become crucial. This project uses two datasets: an SMS Spam Dataset containing labeled spam and ham (non-spam) messages, and a Twitter Hate Speech Dataset containing tweets labeled as hate speech, offensive, or neutral. The text data undergoes several preprocessing steps such as converting to lowercase, removing punctuation and special characters, tokenization, stopword removal, and applying stemming or lemmatization to bring words to their base form. After preprocessing, the text is transformed into numerical form using TF-IDF vectorization, allowing the machine learning algorithms to understand and analyze the content effectively. Various supervised learning models like Random Forest, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) were trained, among which the Random Forest model gave the best results with high accuracy for both spam and hate speech detection tasks. The model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The trained models were then deployed using Flask or FastAPI, providing a simple user interface or API endpoint where users can input text and receive predictions such as “Spam” or “Ham” for messages and “Hate Speech,” “Offensive,” or “Neutral” for tweets. The entire system was developed using Python, with libraries like Pandas, NumPy, Scikit-learn, and NLTK for text processing and machine learning. This solution can be applied to email spam filters, social media moderation, and online safety systems. In the future, the project can be enhanced by integrating deep learning models like LSTM or BERT, supporting multilingual text, and enabling real-time monitoring dashboards. Overall, this project demonstrates how natural language processing and machine learning can work together to automate text analysis, improve content safety, and create a secure digital communication environment.