Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by persistent deficits in social communication and restricted, repetitive patterns of behavior. Early diagnosis is crucial because timely intervention can significantly improve developmental outcomes, adaptive functioning, and quality of life.

Objective: This study reviews current methods for early diagnosis of ASD, evaluates screening and diagnostic tools, identifies barriers to early detection, and explores emerging technologies that may enhance diagnostic accuracy.

Methods: A narrative review methodology was employed using literature published between 2015 and 2025. Research articles, systematic reviews, clinical guidelines, and meta-analyses related to ASD diagnosis, screening instruments, biomarkers, and artificial intelligence applications were examined.

Results: Evidence suggests that ASD-related behavioral signs can be observed as early as 12–18 months of age. Screening tools such as M-CHAT-R/F and STAT demonstrate moderate-to-high sensitivity. Neuroimaging, genetic testing, eye-tracking technologies, and machine learning algorithms show promise as supplementary diagnostic approaches. Despite advancements, disparities in healthcare access, socioeconomic status, and professional training continue to delay diagnosis.

Conclusion: Early diagnosis of ASD remains a global public health priority. Integration of behavioral screening, biomarker research, digital technologies, and multidisciplinary assessment may significantly improve early identification and intervention outcomes.