To enhance the inter-species relationships, this study used Artificial Intelligence and Machine Learning algorithms to optimize human–dog pairings based on personality traits. Recognizing the significance of mental compatibility for canine welfare and well-being, we utilized the Canine Behavioural Assessment and Research Questionnaire (C-BARQ) dataset, which encompasses behavioural metrics for over 12,000 dogs of various breeds. By employing K-means clustering, we categorized these dogs into five distinct personality types: ‘High-energy, Aggressive, Excitable’, ‘Calm, Easily Trainable’, ‘Vigorous and Spirited’, ‘Calm and Compliant’, ‘Energetic and Trainable’, ‘Anxious but Trainable’, and ‘Energetic and Assertive’. Subsequently, we applied the XGBoost algorithm to this labelled data for precise classification. On the human side, we used the results of the OCEAN or Big Five Test to analyse individuals’ behaviours and classify them as ‘Calm Leader,’ ‘Conscientious Organizer,’ ‘Creative Thinker,’ and ‘Overcontrolled.’ The final step involved mapping these analyses to determine the most compatible human–dog pairs and structure relevant inferences. This study aims to improve adoptions by ensuring better mental alignment between dogs and their prospective owners, serving as an innovative metric for evaluating canine welfare and well-being.