Acha is an early indigenous cereal indigenous to most West African countries. The crop, also known as Fonio (Digitaria exilis and Digitaria iburua), is widely valued for its nutritional and economic importance. However, post-harvest processing of Acha remains largely traditional, labor-intensive, and inefficient due to the small grain size and the presence of impurities such as stones, chaff, and husks. The design and development of efficient dehulling, destoning, and dechaffing machines require adequate knowledge of the physical properties of the grain. This study evaluates the moisture-dependent physical properties of Acha (Digitaria exilis) with the aim of providing essential data for the design and optimization of dehulling, destoning, and dechaffing equipment. Standard experimental procedures were applied to determine key dimensional, gravimetric, and frictional properties at five moisture content levels (8, 12, 16, 20, and 24%). The results revealed that grain dimensions—length, width, thickness, arithmetic mean diameter, and geometric mean diameter—increased progressively with rising moisture content, indicating a direct relationship between moisture absorption and grain expansion. Similarly, spatial properties such as sphericity, volume, and surface area exhibited linear growth trends as moisture content increased, suggesting improved grain uniformity and size with higher moisture levels. In contrast, gravimetric properties, including bulk density, true density, and porosity, showed a decreasing trend with increasing moisture content, following non-linear relationships. This reduction is attributed to volume expansion exceeding mass gain during moisture absorption. Frictional properties, namely angle of repose and angle of internal friction, increased consistently across all tested surfaces (steel, wood, and glass), indicating greater resistance to flow at higher moisture levels. Overall, the findings demonstrate that moisture content significantly influences the physical properties of Acha grains. These variations are critical for determining machine design parameters such as hopper inclination, sieve selection, and processing efficiency. The study provides valuable baseline data that can enhance the development of efficient and locally adaptable postharvest processing technologies for Acha, thereby supporting improved grain handling, reduced losses, and increased productivity.