With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.