Financial devastation from cancer treatment affects more than one-third of patients, creating a hidden health crisis that can derail recovery and force impossible choices between medical care and basic necessities. This economic burden, termed 'financial toxicity,' correlates directly with worse survival outcomes, yet oncology teams lack practical tools to identify at-risk patients before crisis strikes. A machine learning analysis of 793 cancer patients revealed that 36% experienced financial toxicity, defined as bankruptcy, unpaid medical bills, payment concerns, or debt accumulation within one year of treatment. The researchers developed a random forest algorithm that accurately predicts financial distress risk with 84% sensitivity, enabling clinicians to intervene proactively. Key risk indicators include younger age, lower baseline income, higher medical expenditures, and compromised health status at diagnosis. The model's interpretability through Shapley values allows clinicians to understand precisely which factors drive each patient's risk score. This represents a significant advance in precision oncology beyond traditional biomarkers, extending predictive medicine into socioeconomic domains that profoundly impact treatment outcomes. While promising, the model requires validation across diverse healthcare systems and insurance structures before widespread implementation. The single-survey design also limits understanding of how financial toxicity evolves throughout extended treatment courses. Nevertheless, this tool could transform cancer care by enabling early identification of financially vulnerable patients, facilitating timely referrals to financial counselors, and informing treatment decisions that balance clinical efficacy with economic sustainability for individual patients.