Abstract—The development of machine learning models for predicting Autism Spectrum Disorder (ASD) requires meticulous data collection and management processes. This paper delves into the systematic approach to acquiring and managing datasets, emphasizing the quality, accuracy, and diversity of the data collected. It discusses the challenges involved in gathering data, addresses ethical considerations for data use, and outlines best practices in managing and securing data. Proper data management strategies ensure the data's integrity and usability, forming the backbone of a robust machine learning model capable of predicting the likelihood of ASD, thus aiding in early diagnosis and effective intervention.