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Summary: The recent trend in modern power sector is to maintain observability of the grid for its smooth operation under all circumstances. To ascertain this aforementioned criterion, grid operators employ state estimation algorithms with a priori measurement data to determine the current operating states of the grid. The prime ideology behind such algorithms is the presence of an over‐determined class of system with abundant measurement redundancy. With loss of real time measurement data, operators resort to state forecasting‐based solutions. This work focuses on the use of scalable deep learning and machine learning models for appropriate forecasting of operating states both for healthy and contingency scenarios. This work also incorporates a critical comparison between them based on RMSE, MSE, MAE and R‐squared index. To facilitate a better training and to prevent model underfitting, Gaussian copula based synthetic data are incorporated showcasing substantial enhancement in performance indices of the models [GRU (0.7958 → 0.0088), LSTM (1.1173 → 0.1020), SVM (1.7256 → 0.1654) and SNN (2.5381 → 0.1972)]. Such training strategies even under system unobservability with optimal hyper‐parameter tuning of the models can lead to proper forecasting of operating states of the system. A comparative analysis between the neural network models under varying noise scenarios also portrays the efficacy of the proposed GRU model. The proposed architecture can also be implemented for real time state forecasting scheme with computational time in the order of micro (μ) seconds (210.34 μs). Simulation results on IEEE 14 bus system validate these aforementioned propositions. [ABSTRACT FROM AUTHOR]