This study explores the predictive power of genetic signatures in forecasting patient responses to checkpoint inhibitor immunotherapy. By conducting an evaluative analysis of various genetic markers cited in the literature, this research aims to identify which patients are likely to benefit from immunotherapy. Despite the transformative potential of immunotherapy, a significant proportion of patients fail to respond favorably, underscoring the urgent need for precise predictive tools. Utilizing advanced machine learning techniques, the study generates predictive models that analyze accuracy and area under the curve (AUC) metrics to determine the most effective genetic signatures. These findings enhance our understanding of gene behavior in response to treatment and improve the selection process for suitable candidates for immunotherapy, thereby optimizing treatment outcomes.