Background: Cancer patients are frequent users of the emergency department (ED); preventing such visits is important for improving the quality of cancer care and decreasing health care system burden. This study describes oncology ED visits at a single institution, and seeks to define a novel definition of potentially preventable ED visits (PPEDs), particularly considering novel cancer therapies. It also uses machine learning (ML) to create early models to predict patients at highest risk of PPEDs. Methods: A retrospective study of ED visits by oncology patients between April 1^st, 2019 and April 1^st, 2021 at a single academic institution was completed. PPEDs were defined as ED visits resulting in discharge from the ED or an admission of less than 48 hours. PPEDs versus non-PPEDs were evaluated using descriptive statistics, logistic regression, and machine learning (ML) modelling. Results: 17% of cancer patients (n=6,689) had 13,415 ED visits during the study period. PPEDs differed in characteristics from non-PPEDs. Stage 1-3 patients with breast cancer, and those on IV systemic therapy were more likely to have PPEDs.Logistic regression models of pre-ED visit variables showed that stage, distance, and neoadjuvant systemic therapy were associated with PPEDs. Logistic regression of ED visit-related variables revealed that genitourinary, eye, or skin-related diagnoses were associated with PPEDs. The highest-performing ML model scored an AUC of 0.819, and combined pre-ED visit and ED visit related variables; stage 1-3 breast cancer patients, all IV chemotherapy patients, and stage 4 patients living within 15km of the ED, or with GI, oral or female gynecologic cancers were associated with PPEDs. ED diagnoses of “cancer” and MSK issues were associated with PPEDs. For those patients on IV systemic therapy specifically, having breast cancer, having a palliative consult within 60 days of the ED visit, or presenting with ED diagnoses of toxicities from therapy or blood disorders, pain, or rash were associated with PPEDs. Conclusions: Our novel definition of PPEDs versus non-PPEDs appears reasonable, and using machine learning has helped to describe groups of patients at highest likelihood of such visits at our institution. Future work to validate and advance these models into clinical prediction tools and algorithms could help proactively target high risk patients, and ideally decrease preventable ED visits in oncology.