As 6G networks enable real-time data acquisition from millions of embedded sensors, the challenge of efficiently processing vast multi-modal datasets becomes paramount. This chapter explores how memcomputing, specifically through Memristor Cellular Neural Networks (M-CellNNs), can address these challenges by diverging from conventional compute-centric models. By leveraging volatile and non-volatile memristors, M-CellNNs can achieve high-speed, energy-efficient data processing directly at the sensor level, addressing challenges related to execution time, data privacy, and compatibility. We demonstrate the multitasking and memcomputing capabilities of M-CellNNs for simultaneous image processing, while emphasizing the need for novel software frameworks and mapping strategies to facilitate seamless integration of these advanced computing architectures. This discussion highlights M-CellNNs as a promising approach for scalable, robust, real-time data processing in 6G applications, with the potential to improve performance, accuracy, and energy efficiency.