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In response to the demand for impartial and precise personality testing, this study presents a unique multi-modal method for predicting personality traits in collaborative settings. Conventional approaches that depend on surveys frequently create biases, which has led to the investigation of raw, subconscious open writing as a rich source of personality information. This study uses deep learning algorithms in conjunction with the stream of consciousness storytelling approach to uncover personality traits by utilizing both textual and gestural data. We use BERT word embedding to improve contextual understanding and convolutional networks for the textual component. Compared to earlier methods, this methodology offers a more dependable way for text-based personality evaluation. Moreover, we present facial recognition as an extra factor for personality evaluation, providing a whole framework with a wide range of uses. We conducted studies in a collaborative setting to assess the effectiveness of our strategy, and we obtained encouraging findings. This multimodal method changes the way people collaborate and opens doors to a wide range of applications, such as mental health diagnosis, job interviews, and forensic investigations. A thorough grasp of personality features is expected to improve personalization and cooperation, leading to more efficient collaboration and improved decision-making.