Because of the quick proliferation of social media platforms, there has been an unprecedented amount of content created by users. As a result, the automatic classification of emotions has become a critical job in order to comprehend the public's feelings, signals related to mental health, and patterns of behavior online. It is sometimes difficult for traditional text-based methods to fully understand the emotional context of postings made online, particularly when users communicate their thoughts by using a combination of pictures, descriptions, emojis, and visual indicators. A multimodal fusion framework that combines both visual features and verbal representations in order to improve the accuracy and robustness of emotion categorization in online posts is proposed in this study. The model utilizes sophisticated deep learning architectures that combine convolutional neural networks (CNNs) and vision transformers (ViT) for the purpose of extracting features from images, as well as transformer-based language models such as BERT and RoBERTa for the purpose of comprehending text. In order to identify the best approach for aligning diverse modalities, a variety of fusion techniques are being assessed. These include early fusion, late fusion, and hybrid attention-based fusion, among others. Experiments are carried out on benchmark multimodal emotion datasets, which provide evidence that multimodal fusion much outperforms unimodal models, particularly when it comes to identifying nuanced emotions that are dependent on context, such as fear, disgust, and mixed affective states. The findings demonstrate that the integration of visual and textual clues is essential for more accurately representing the intricacies of human emotional expression in digital settings. This study has made a significant contribution to the field of emotional computing, and it has practical applications in the fields of social media analytics, monitoring of mental health, and the development of systems that are able to propose information that is tailored to the individual user.