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Background: Basal cell carcinoma (BCC) is the most common type of skin cancer in the Caucasian population. Currently, invasive biopsy is the only way of establishing the histological subtype (HST) that determines the treatment options. Our study aimed to evaluate whether optically guided high-frequency ultrasound (OG-HFUS) imaging could differentiate aggressive HST BCCs from low-risk tumors., Methods: We conducted prospective clinical and dermoscopic examinations of BCCs, followed by 33 MHz OG-HFUS imaging, surgical excision, and a histological analysis. We enrolled 75 patients with 78 BCCs. In total, 63 BCCs were utilized to establish a novel OG-HFUS risk classification algorithm, while 15 were employed for the validation of this algorithm. The mean age of the patients was 72.9 ± 11.2 years. Histology identified 16 lesions as aggressive HST (infiltrative or micronodular subtypes) and 47 as low-risk HST (superficial or nodular subtypes). To assess the data, we used a one-sided Fisher's exact test for a categorical analysis and a Receiver Operating Characteristic (ROC) curve analysis to evaluate the diagnostic accuracy., Results: OG-HFUS distinguished aggressive BCC HSTs by their irregular shape ( p < 0.0001), ill-defined margins ( p < 0.0001), and non-homogeneous internal echoes ( p = 0.004). We developed a risk-categorizing algorithm that differentiated aggressive HSTs from low-risk HSTs with a higher sensitivity (82.4%) and specificity (91.3%) than a combined macroscopic and dermoscopic evaluation (sensitivity: 40.1% and specificity: 73.1%). The positive and negative predictive values (PPV and NPV, respectively) for dermoscopy were 30.2% and 76.8%, respectively. In comparison, the OG-HFUS-based algorithm demonstrated a PPV of 94.7% and an NPV of 78.6%. We verified the algorithm using an independent image set, n = 15, including 12 low-risk and 3 high-risk (high-risk) with two blinded evaluators, where we found a sensitivity of 83.33% and specificity of 91.66%., Conclusions: Our study shows that OG-HFUS can identify aggressive BCC HSTs based on easily identifiable morphological parameters, supporting early therapeutic decision making.