According to the World Health Organization(WHO), malaria is estimated to have killed 627,000 people and infected over 241 million people in 2020 alone, a 12% increase from 2019. Microscopic diagnosis of blood cells is the standard testing procedure to diagnose malaria. However, this style of diagnosis is expensive, time-consuming, and greatly subjective to human error, especially in developing nations that lack well-trained personnel to perform high-quality microscopy examinations. This paper proposes Mass-AI-Scope (MAIScope): a novel, low-cost, portable device that can take microscopic images and automatically detect malaria parasites with embedded AI. The device has two subsystems. The first subsystem is an on-device multi-layered deep learning network, that detects red blood cells (RBCs) from microscopic images, followed by a malaria parasite classifier that recognizes malaria parasites in the individual RBCs. The testing and validation demonstrated a high average accuracy of 89.9% for classification and average precision of 61.5% for detection models using TensorFlow Lite while addressing limited storage and computational capacity. This system also has cloud synchronization, which sends images to the cloud when connected to the Internet for analysis and model improvement purposes. The second subsystem is the hardware which consists of components like Raspberry Pi, a camera, a touch screen display, and an innovative low-cost bead microscope. Evaluation of the bead microscope demonstrated similar image quality with that of expensive light microscopes. The device is designed to be portable and work in remote environments without the Internet or power. The solution is extensible to other diseases requiring microscopy and can help standardize automation of disease diagnosis in rural parts of developing nations.