Schizophrenia is a chronic debilitating mental illness that has a lifetime prevalence of approximately 1%. Despite extensive research, the exact etiopathogenesis of schizophrenia remains unclear. Up to a third of patients with schizophrenia do not respond adequately to first-line antipsychotic treatment and close to 20% eventually progress to ultra-treatment resistance with no response to multiple antipsychotic medications. Identifying biomarkers to predict treatment outcomes will enhance the treatment experience and reduce disability by enabling judicious utilization of available resources and drive targeted therapeutic approaches. This chapter explores the role of various biological characteristics, such as genetic, neuroimaging, neurophysiological, and biochemical markers, in predicting antipsychotic treatment response in schizophrenia. Emerging technologies and machine learning approaches are also discussed for their potential in increasing predictive accuracy.