Lower limb amputations pose significant challenges for patients, with over 150,000 cases annually in the U.S., leading to a high demand for effective prosthetics. However, only 43% of lower limb prosthetic users report satisfaction, primarily due to issues with socket fit, which is critical for comfort, stability, and preventing injury. This study presents a deployable sensing system for potentially real-time monitoring of prosthetic socket fit by using pressure sensors and convolutional neural networks (CNNs) to analyze the pressure distribution within the socket. A novel CNN architecture, utilizing both dilated and strided convolutions, is proposed to effectively capture spatial-temporal patterns in multivariate timeseries data, which is processed as an image. The system was designed for edge deployment on the Sony Spresense microcontroller, maintaining a small model size while achieving high accuracy. Results show that the CNN models, particularly those optimized with the stochastic gradient descent (SGD), demonstrated robustness and high transferability. This system provides a cost-effective, portable solution to improve prosthetic fit, enhancing patient care and preventing gait-related injuries.