Social media memes have become a powerful form of digital communication, combining images and text to convey humor, social commentary, and sometimes harmful content. This paper presents a multimodal approach using a fine-tuned CLIP model to analyze textembedded images in the CASE 2025 Shared Task. We address four subtasks: Hate Speech Detection, Target Classification, Stance Detection, and Humor Detection. Our method effectively captures visual and textual signals, achieving strong performance with precision of 80% for the detection of hate speech and 76% for the detection of humor, while stance and target classification achieved a precision of 60% and 54%, respectively. Detailed evaluations with classification reports and confusion matrices highlight the ability of the model to handle complex multimodal signals in social media content, demonstrating the potential of vision-language models for computational social science applications.