Calcium activity is crucial for numerous neuronal processes, influencing synaptic functions, neuronal communication, and overall brain activity. These dynamics offer valuable insights into both normal brain physiology and various neuropsychiatric disorders, including schizophrenia, autism, epilepsy, and Alzheimer’s disease. Recently developed brain organoid models derived from patient induced pluripotent stem cells (iPSCs) present unique opportunities to monitor neurodevelopmental dynamics in real time. However, existing calcium imaging techniques have not been optimized to accurately detect calcium transients and extract meaningful data from brain organoid models. This paper introduces a sophisticated computational pipeline designed for these applications. Key steps in our analysis include data cropping to reduce computational load and noise, stabilization to correct motion artifacts, and enhanced peak detection algorithms adapted from MATLAB-based tools. We demonstrate the effectiveness of our pipeline through rigorous statistical analyses of a brain organoid model of schizophrenia, revealing significant differences in individual cell calcium activity dynamics and network features between patient and control groups. This robust tool will not only aid in identifying disease mechanisms but also provide valuable insights into the effects of pharmacological interventions on neuronal activities across various neuropsychiatric disorders.