Ongoing backscatter communications and localisation research have been able to obtain incredibly accurate results in controlled environments. The main issue with these systems is faced in complex RF environments. This paper investigates concurrent localization and ambient radio frequency (RF) energy harvesting using backscatter communication systems for Internet of Things networks. Dynamic real-world environments introduce complexity from multipath reflection and shadowing, as well as interference from movements. A machine learning framework leveraging K-Nearest Neighbors and Random Forest classifiers creates robustness against such variability. Historically, received signal measurements construct a location fingerprint database resilient to perturbations. The Random Forest model demonstrates precise localization across customized benches with programmable shuffling of chairs outfitted with RF identification tags. Average precision accuracy exceeds 99% despite deliberate placement modifications, inducing signal fluctuations emulating mobility and clutter. Significantly, directional antennas can harvest over −3 dBm, while even omnidirectional antennas provide −10 dBm—both suitable for perpetually replenishing low-energy electronics. Consequently, the intelligent backscatter platform localizes unmodified objects to customizable precision while promoting self-sustainability.