Forest inventory (FI) surveys are cumbersome when field measurements are performed by manual means. We propose a semi-automated data collection approach using handheld mobile laser scanning (HMLS) to estimate and map key FI parameters. To this end, machine learning (e.g., random forest classifier for tree detection) and innovative algorithms (e.g., ellipse fitting for diameter estimation of noncircular trees) were used for the first time in FI surveying. After surveying nine plots, we compared HMLS-derived data against the field reference. HMLS-derived tree diameters (DBHs) were strongly correlated with the reference data at the single-tree level (r= 0.93–0.99; p< 0.001). At the plot level, HMLS slightly overestimated DBHs in complex plots due to the influence of undergrowth and creepers on trunks. Yet, no statistically significant difference was found between the two datasets (p> 0.05). Overall, HMLS was concluded as efficient and effective tool for FIs, even if used alone.