The criteria for measuring soil compaction parameters, such as optimum moisture content and maximum dry density, play an important role in construction projects. On construction sites, base/sub-base soils are compacted at the optimal moisture content to achieve the desirable level of compaction, generally between 95% and 98% of the maximum dry density. The present technique of determining compaction parameters in the laboratory is a time-consuming task. This study proposes an improved hybrid intelligence paradigm as an alternative tool to the laboratory method for estimating the optimum moisture content and maximum dry density of soils. For this purpose, an advanced version of the grey wolf optimiser (GWO) called improved GWO (IGWO) was integrated with an adaptive neuro-fuzzy inference system (ANFIS), which resulted in a high-performance hybrid model named ANFIS-IGWO. Overall, the results indicate that the proposed ANFIS-IGWO model achieved the most precise prediction of the optimum moisture content (degree of correlation = 0.9203 and root mean square error = 0.0635) and maximum dry density (degree of correlation = 0.9050 and root mean square error = 0.0709) of soils. The outcomes of the suggested model are noticeably superior to those attained by other hybrid ANFIS models, which are built with standard GWO, Moth-flame optimisation, slime mould algorithm, and marine predators algorithm. The results indicate that geotechnical engineers can benefit from the newly developed ANFIS-IGWO model during the design stage of civil engineering projects. The developed MATLAB models are also included for determining soil compaction parameters.