The application of Semi Autogenous Grinding strategy in mineral beneficiation engineering is well defined and established unit operation that is qualified for liberation of valuable final products from various rock ores of various kinds. The TENCAN GQM series Ball Mill equipment was deployed throughout our investigation to learn on optimum setting factors that are crucial in achieving effective product quality outcome during coal beneficiation. Therefore, a Semi-autogenous grinding methodology was deployed for grinding five (5) different coal samples into a product stream ranging in particle size from below [−600μm, +38μm] through an optimized procedure that syndicate three(3) control parameters being the {A − powder filling (fc), B − ball loading (JB) and C − residence time or grinding duration (t)}. Moreover, to analyse the grinding kinetics during interaction to produce coal mass recovery at desirable particle size specification relevant for specific manufacturing industry. However, initially the coal sample material was acquired and characterized via three spectroscopy techniques {x – ray diffraction (XRD), x – ray fluorescence (XRF), scanning electron microscopy (SEM)}, to establish contrast of each sample material in composition, morphology, hardness, and the relative abundance of occurring species entrenched within the coal structure (inorganic matter). Hence using the Proximate testing, Ultimate testing, and petrography analysis the examination was possible for each sample type collected. The coal samples that were initially collected were assorted in particle sizes classification and were grouped as run-of-mine coals (−200mm), Cobbles (−75mm, +40mm), Nuts (−40mm, +25mm), Peas (−32mm, +14mm) and fines (−14mm) according to our laboratory sieve specifications. However, the acquired coal species were initially subjugated to a hardness testing using the Hardgrove grindability Index device that reported a variable coal grindability index value with material hardness increased from Nuts (60.37), Cobbles (64.58), ROM (66.84), Fines (66.99) to Peas (67.37) which are relatively soft compared to other coal samples. Process engineers, researchers and students in different organizations studying metallurgy and coal beneficiation etc., must be informed about factors affecting coal material preparation innovations and the health-safety risk encounter by the mineral engineer personal during handling and processing. Moreover, production efficacy is significantly improved through adopting optimized model functions that accurately increase the product recovery at lower power rating for the cumulative production path hence relevantly reducing the cost(s) of exacerbated by tedious and unnecessary methods.