This study uses machine learning to address the urgent need for early detection and intervention in autism spectrum disorder (ASD). Autism Spectrum Disorder (ASD), a disorder of neural development affecting cognition and social interaction, necessitates customized interventions. Our research uses various screening assessments in early infancy as datasets to train for neural network algorithms, with the objective of identifying patterns associated with ASD. The suggested methodology utilizes predictive machine learning techniques to assess the ASD spectrum, accounting for its diverse pitches. Utilizing sophisticated screening instruments and an innovative methodology, we seek to establish a dependable system for the early detection of ASD. Ethical considerations, data privacy, and algorithmic openness are essential. The studies seek to foster collaboration among experts in neurodevelopment, psychological science, and data science to build precise tools for earlier identification of ASD. The use of machine learning, an effective tool, enhances the knowledge of healthcare workers. The research results underscore technology's capacity to transform initial measures, leading to enhanced outcomes for those on the autistic spectrum. It provides this assistance to enhance the dialogue at the gathering, thereby promoting interaction at the nexus of healthcare and digital technology.