Advanced detection methods are required to the increase in hate speech, cyberbulling and online threats on social media. Recent studies demonstrate the efficiency of transformer based framework like BREE-HD for spotting threat on twitter and multimodal model like CLIP for detecting hate in memes. Fuzzy logic, deep learning and machine learning are combined to improve classification accuracy and handle sentiment ambiguity. Furthermore, in order to reduce biases in detection algorithms, fairness aware training approaches are essential. The impact of social media on actual events highlights the necessity of promptly observing potentially dangerous patterns. Using knowledge from transformer models, deep convolutional network, pandemic time cyber behavior analysis and fairness constrained learning, this study examine current AI based approaches, difficulties and development in hate speech and threat identification. Our results support multimodal detection techniques that are ethically acceptable in order to guarantee safer and more welcoming online spaces.