Voice cloning technology has significantly improved over the past few years, enabling the generation of synthetic voices in different languages and allowing for voice cloning. However, AI-cloned voices face limitations in dataset availability for Southeast Asian languages, making it difficult to accurately generate speech patterns due to variations in pronunciation and intonation. This study aims to evaluate AI-cloned voices based on their pitch, tone, and intonation, identifying which best resembles the original voice. To achieve this, 1,200 audio signals were collected from YouTube search queries and underwent pre-processing techniques. The results revealed that Rask performed best in pitch, with a Mean F0 score of 203.07 Hz, indicating excellent performance. In tone analysis, Altered achieved a score of -207.16, which falls within the <5 range, signifying excellent speech recognition. For intonation, Rask recorded a score of 197.16, demonstrating superior accuracy in analyzing vibration rates. These findings highlight the variations in pitch, tone, and intonation due to regional accents and voice quality. This study serves as a benchmark for further optimization, aiming to improve the accuracy and adaptability of AI-cloned voices in diverse linguistic contexts.