Autism Spectrum Disorder (ASD) is a complex, long-lasting neurodevelopmental condition characterised by communication difficulties. However, early identification of ASD may aid in the design of appropriate treatments to improve communicative development. This study employs association rule mining algorithms, such as apriori, predictive apriori, and Tertius, to identify age-specific behavioural markers across four developmental stages: toddlers, children, adolescents, and adults. These algorithms identified several key behavioural indicators associated with ASD, including pretending games (A5), activity switching (A4), difficulty in making friends (A10), difficulty in conversations (A5), detection of listener boredom (A6) and easy reading of emotions (A9). The apriori algorithm achieved the highest confidence of 100.00% for the adolescent dataset, whereas the predictive apriori algorithm demonstrated the highest accuracy of 99.50% for the toddler dataset, 99.40% for the child dataset and 99.39% accuracy for adult dataset. In contrast, the Tertius algorithm showed the highest confirmation rate of 66.8% for the child dataset, although it required the most time for manipulation across all datasets. Our findings demonstrate that rule mining effectively uncovers clinically relevant patterns, with behavioural questions proving more significant than demographic factors, such as gender or birth complications. Furthermore, no connection was observed between ASD and a history of jaundice at birth. Performance comparisons among the three algorithms are also presented. The rules generated through our investigation will help physicians in the early detection of ASD, thus paving the way for a timely and targeted intervention.