This paper presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that the Quantum Approximate Optimization Algorithm (QAOA) can be substantially accelerated under both ideal and noisy conditions. The study focuses on the parameter transferability between different MaxCut instances, leveraging graph representation learning techniques to predict donor graphs that can be used to transfer optimal parameters. The key findings include the identification of two structural graph features—subgraph composition and graph parity—that predict good transferability between MaxCut instances. The paper evaluates five different graph embedding techniques, including Graph2Vec, GL2Vec, wavelet characteristic, SF, and FEATHER, to determine their effectiveness in predicting donor candidates. The results show that Graph2Vec performs best in predicting donor graphs, achieving high accuracy in parameter transferability. The study also demonstrates that the transferred parameters maintain effectiveness under noisy conditions, supporting their use in real-world quantum applications. The framework significantly reduces the number of iterations required for parameter optimization, achieving an order of magnitude speedup. The results highlight the potential of graph embedding techniques in accelerating QAOA for both ideal and noisy quantum computing scenarios.This paper presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that the Quantum Approximate Optimization Algorithm (QAOA) can be substantially accelerated under both ideal and noisy conditions. The study focuses on the parameter transferability between different MaxCut instances, leveraging graph representation learning techniques to predict donor graphs that can be used to transfer optimal parameters. The key findings include the identification of two structural graph features—subgraph composition and graph parity—that predict good transferability between MaxCut instances. The paper evaluates five different graph embedding techniques, including Graph2Vec, GL2Vec, wavelet characteristic, SF, and FEATHER, to determine their effectiveness in predicting donor candidates. The results show that Graph2Vec performs best in predicting donor graphs, achieving high accuracy in parameter transferability. The study also demonstrates that the transferred parameters maintain effectiveness under noisy conditions, supporting their use in real-world quantum applications. The framework significantly reduces the number of iterations required for parameter optimization, achieving an order of magnitude speedup. The results highlight the potential of graph embedding techniques in accelerating QAOA for both ideal and noisy quantum computing scenarios.