Cry analysis and pain assessment in neonates and infants is a crucial aspect of paediatric healthcare. Pain in neonates and infants is often underdiagnosed and undertreated, which can lead to long-term negative consequences on their health and development. Traditional pain assessment methods, such as behavioural observation scales, have limitations in accuracy and reliability. Cry analysis has emerged as a promising non-invasive method for pain assessment in neonates and infants. Machine learning techniques have been applied to cry analysis for pain assessment, which has shown great potential in improving the accuracy and reliability of pain assessment. This research has significant implications for improving the quality of care provided to neonates and infants. A reliable and comprehensive automated system for pain assessment can help healthcare professionals make informed decisions about pain management strategies, leading to improved outcomes for these vulnerable populations.