Emotion recognition based multimodal data (e.g., video, audio, text, etc.) is a highly demanding and significant research field with numerous applications. This research rigorously explores model-level fusion to discover the best multifunctional model combining audio and visual modalities for emotion identification. Specifically, it proposes novel feature extractor networks for both video and audio data. This research presents a comprehensive approach to multimodal emotion recognition, utilizing state-of-the-art feature extraction methods tailored to each modality. For text data, we implement the Assimilated N gram Approach (ANA) to effectively capture contextual information. Audio features are extracted using Mel-Frequency Cepstral Coefficients (MFCC), ideal for capturing spectral characteristics in speech. Visual features are derived using SqueezeNet, a deep learning architecture optimized for efficient and informative visual data representation. To integrate the extracted features from text, audio, and visual modalities, we propose a multimodal data fusion strategy that combines information across modalities, thereby enhancing the overall representation of emotional cues. In the classification stage, we employ Support Vector Machine (SVM), a robust and effective classification algorithm known for its ability to handle high-dimensional data and perform well in diverse scenarios. Using the Multimodal Emotion Lines Dataset (MELD), our approach achieved an accuracy of 98.1%, precision of 98.85%, recall of 98.75%, and F-measure of 98.3. These results highlight the effectiveness of our multimodal framework in emotion recognition tasks.