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Purpose To investigate whether a novel iterative cone-beam computed tomography (CBCT) reconstruction algorithm reduces metal artifacts in head and neck patient images. Method An anthropomorphic phantom and 35 patients with dental metal prostheses or implants were analyzed. All CBCT images were acquired using a TrueBeam linear accelerator and reconstructed with a Feldkamp–Davis–Kress algorithm-based CBCT (FDK-CBCT) and an iterative CBCT algorithm. The mean Hounsfield unit (HU) and standard deviation values were measured on the tongue near the metal materials and the unaffected region as reference values. The artifact index (AI) was calculated. For objective image analysis, the HU value and AI were compared between FDK-CBCT and iterative CBCT images in phantom and clinical studies. Subjective image analyses of metal artifact scores and soft tissue visualizations were conducted using a five-point scale by two reviewers in the clinical study. Results The HU value and AI showed significant artifact reduction for the iterative CBCT than for the FDK-CBCT images (phantom study: 389.8 vs.−10.3 for HU value, 322.9 vs. 96.2 for AI, FDK-CBCT vs. iterative CBCT, respectively; clinical study: 210.3 vs. 69.0 for HU value, 149.6 vs. 70.7 for AI). The subjective scores in the clinical patient study were improved in the iterative CBCT images (metal artifact score: 1.1 vs. 2.9, FDK-CBCT vs. iterative CBCT, respectively; soft tissue visualization: 1.8 vs. 3.6). Conclusions The iterative CBCT reconstruction algorithm substantially reduced metal artifacts caused by dental metal prostheses and improved soft tissue visualization compared to FDK-CBCT in phantom and clinical studies.