Background: Nose-to-brain (N2B) drug delivery offers unique advantages over intravenous methods; however, the delivery efficiency to the olfactory region using conventional nasal devices and protocols is low. This study proposes a new strategy to effectively deliver high doses to the olfactory region while minimizing dose variability and drug losses in other regions of the nasal cavity. Materials and Methods: The effects of delivery variables on the dosimetry of nasal sprays were systematically evaluated in a 3D-printed anatomical model that was generated from a magnetic resonance image of the nasal airway. The nasal model comprised four parts for regional dose quantification. A transparent nasal cast and fluorescent imaging were used for visualization, enabling detailed examination of the transient liquid film translocation, real-time feedback on input effect, and prompt adjustment to delivery variables, which included the head position, nozzle angle, applied dose, inhalation flow, and solution viscosity. Results: The results showed that the conventional vertex-to-floor head position was not optimal for olfactory delivery. Instead, a head position tilting 45–60° backward from the supine position gave a higher olfactory deposition and lower variability. A two-dose application (250 mg) was necessary to mobilize the liquid film that often accumulated in the front nose following the first dose administration. The presence of an inhalation flow reduced the olfactory deposition and redistributed the sprays to the middle meatus. The recommended olfactory delivery variables include a head position ranging 45–60°, a nozzle angle ranging 5–10°, two doses, and no inhalation flow. With these variables, an olfactory deposition fraction of 22.7 ± 3.7% was achieved in this study, with insignificant discrepancies in olfactory delivery between the right and left nasal passages. Conclusions: It is feasible to deliver clinically significant doses of nasal sprays to the olfactory region by leveraging an optimized combination of delivery variables.