Memes have evolved into a powerful tool for social interaction on platforms like Twitter, Instagram, Facebook, Pinterest, where they communicate complex emotions through a blend of images, text, and emojis. In this research, we propose a hybrid deep learning model that not only processes the textual components but also integrates the expressive use of emojis to better capture the nuanced emotions embedded in memes. While traditional sentiment analysis approaches often fall short in understanding these intricate emotional cues, our deep learning approach aims to overcome these challenges. By leveraging multimodal features—the textual content alongside visual cues like emojis. We provide a more holistic method for detecting and classifying emotions in memes, enhancing the accuracy of sentiment detection. Our research also distinguishes itself by focusing specifically on harmful, offensive, and trolling content, using hybrid deep learning models that integrate both Natural Language Processing (NLP) techniques and image recognition. This approach not only enables the system to detect sentiment but also classifies different types of toxic behavior (like trolling) that are prevalent in meme culture. The dataset used for experimentation contains a range of memes annotated with these emotional and behavioral labels. In sum, this study contributes to the existing literature by presenting an advanced, context-aware method for meme classification, emphasizing the importance of both text and visual elements. The experiments conducted showcase the effectiveness of our model in accurately detecting complex emotional expressions, particularly in memes designed to provoke or offend. This research pushes the boundaries of meme analysis, helping to mitigate online toxicity while providing new tools for sentiment analysis.