Non-destructive evaluation techniques are increasingly recognised as effective alternatives to destructive testing for estimating the compressive strength of lightweight aggregate concrete (LWAC). Among these, ultrasonic pulse velocity (UPV) is a well-established and widely employed method, characterised by its rapidity, non-invasiveness, and relative simplicity of implementation. In this study, an experimental dataset comprising 640 core segments from 160 cylindrical specimens, provided for analysis, was investigated. Each segment was described by physical and processing variables, including lightweight aggregate and concrete densities, casting and vibration times, experimental dry density, and P-wave velocity obtained through UPV testing. A segregation index (SI), derived from UPV measurements and defined as the ratio of local to mean P-wave velocity within each specimen, was also considered, following approaches previously suggested in the literature. A range of machine learning techniques was applied to assess the predictive capacity of local P-wave velocity and SI. Most ensemble-based methods and support vector regression achieved the highest accuracy when SI was excluded, indicating that its contribution was redundant. By contrast, Gaussian process regression showed slight improvements when SI was included. The results confirmed that the P-wave velocity measured by UPV testing is a reliable non-destructive predictor of compressive strength in LWAC. At the same time, the added value of SI remains negligible under conditions of low segregation, as reflected by SI values above 0.8. These findings highlight the practical potential of integrating UPV-based measurements with data-driven modelling to enhance the reliability of concrete characterisation and quality control.