Several smart grid applications have recently been devised in order to timely perform supervisory functions along with self‐healing and adaptive countermeasures based on system‐wide analysis, with the ultimate goal of reducing the risks associated with potentially insecure operating conditions. Real‐time transient stability assessment (TSA) belongs to this type of applications, which allows deciding and coordinating pertinent corrective control actions depending on the evolution of post‐fault rotor‐angle deviations. This study presents a novel approach for carrying out real‐time TSA based on prediction of area‐based centre‐of‐inertia (COI) referred rotor angles from phasor measurement unit (PMU) measurements. Monte Carlo‐based procedures are performed to iteratively evaluate the system transient stability response, considering the operational statistics related to loading condition changes and fault occurrence rates, in order to build a knowledge database for PMU and COI‐referred rotor‐angles as well as to screen those relevant PMU signals that allows ensuring high observability of slow and fast dynamic phenomena. The database is employed for structuring and training an intelligent COI‐referred rotor‐angle regressor based on support vector machines [support vector regressor (SVR)] to be used for real‐time TSA from selected PMUs. Besides, the SVR is optimally tuned by using the swarm variant of the mean‐variance mapping optimisation. The proposal is tested on the IEEE New England 39‐bus system. Results demonstrate the feasibility of the methodology in estimating the COI‐referred rotor angles, which enables alerting about real‐time transient stability threats per system areas, for which a transient stability index is also computed.