ABSTRACT : In response to the pressing need for effective crowd management solutions for robotics, this study delves into the creation of a deep learning-based scene and face recognition system tailored for robotic crowd management. The primary objective is to develop a system that not only exhibits high levels of accuracy and efficiency but also adheres to ethical principles. This is achieved through the integration of deep learning models with multi-modal fusion approaches, allowing for the swift and comprehensive assessment of optical, auditory, and depth data to identify potential hazards and maintain crowd control. At the heart of this research lies the meticulous gathering and preparation of data. Establishing a broad and representative dataset is paramount to the success of the system. Through comprehensive dataset collection methods, encompassing diverse scenarios and environments, we ensure that the model is robust and capable of handling real-world crowd management challenges effectively. Drawing inspiration from the methodology of the Tensorflow Object Counting CSRNet, our approach follows a structured framework encompassing various stages. Data preparation involves preprocessing input data and generating density maps using Gaussian distribution functions, laying the foundation for accurate crowd assessment. A custom data generator class facilitates efficient data handling, ensuring smooth processing of batches of images and corresponding density maps. The model architecture is meticulously designed to leverage the strengths of deep learning, incorporating convolutional layers for feature extraction, and reshaping to generate output heat maps. During the training phase, a custom loss function is employed to compute the root mean square error (RMSE), optimizing model performance. Training and validation are conducted over multiple epochs, utilizing the custom data generator to enhance efficiency. Following training, the model undergoes rigorous evaluation on sample crowd images, with predictions compared against ground truth density maps to assess its accuracy and efficacy. Visualization techniques are employed to provide qualitative insights into the model's performance, facilitating a deeper understanding of its capabilities. Finally, this study presents a comprehensive approach to robotic crowd management through the integration of deep learning and multi-modal fusion techniques.