The growing use of social media has increased online harassment, cyberhate, and the use of offensive language. This poses significant challenges for effectively detecting and addressing such issues. Natural Language Processing (NLP) has seen considerable advancements; however, automatically identifying offensive language remains a complex task due to the ambiguous and informal nature of user-generated content and the social context in which it occurs. In this thesis, our goal is to develop methods for automatic detection of offensive language in social media. Multiple classification algorithms, including Multinomial Naive Bayes, Gaussian Naive Bayes, SVM, Logistic Regression, and LSTM, are implemented and evaluated. Key measures including accuracy, F1 score, and AUC score are used to evaluate how well these algorithms work. Results show that the Random Forest Classifier obtains an AUC score of 0.65 and an accuracy of 0.82 without word2vec. On the other hand, LSTM demonstrates a competitive AUC score of 0.78 when compared to the Random Forest Classifier. These findings provide insights into the effectiveness of different algorithms for offensive language detection. The research contributes to the field by providing valuable tools and insights to enhance Turkish language processing and prioritize online safety, particularly in combating cyberbullying and fostering a tolerant online environment. The findings also pave the way for future research endeavors in natural language processing and have practical implications for protecting individuals and promoting a secure online space.