This paper explores the application of transfer learning (TL) to optimize parameters of the Quantum Approximate Optimization Algorithm (QAOA) for solving combinatorial optimization problems (COPs). The study investigates how pre-trained QAOA parameters from one problem instance can be reused to solve different COPs, reducing the need for classical optimization. The research focuses on several COPs, including the Traveling Salesman Problem (TSP), Bin Packing Problem (BPP), Knapsack Problem (KP), Weighted Maximum Cut (MaxCut), Maximum Independent Set (MIS), and Portfolio Optimization (PO). The goal is to find optimal β and γ parameters for p layers of QAOA and assess their transferability across different problems. The study demonstrates that BPP produces the most transferable parameters, maintaining a probability of finding the optimal solution above a quadratic speedup over random guessing for up to 42 qubits and p = 10 layers. Experiments on quantum hardware platforms such as IonQ Harmony, Aria, Rigetti Aspen-M-3, and IBM Brisbane show that IonQ Aria yields the best overlap with the ideal probability distribution. Additionally, the study shows that cross-platform TL is possible using the D-Wave Advantage quantum annealer with BPP parameters, achieving improved performance for MIS problems with up to 170 qubits. The results indicate that QAOA parameters can generalize well across different COPs and annealing protocols. The study also highlights the benefits of TL in reducing the computational resources required for classical optimization, particularly for large problems. The findings suggest that TL can be a valuable tool for improving the efficiency and effectiveness of quantum algorithms in solving complex optimization problems.This paper explores the application of transfer learning (TL) to optimize parameters of the Quantum Approximate Optimization Algorithm (QAOA) for solving combinatorial optimization problems (COPs). The study investigates how pre-trained QAOA parameters from one problem instance can be reused to solve different COPs, reducing the need for classical optimization. The research focuses on several COPs, including the Traveling Salesman Problem (TSP), Bin Packing Problem (BPP), Knapsack Problem (KP), Weighted Maximum Cut (MaxCut), Maximum Independent Set (MIS), and Portfolio Optimization (PO). The goal is to find optimal β and γ parameters for p layers of QAOA and assess their transferability across different problems. The study demonstrates that BPP produces the most transferable parameters, maintaining a probability of finding the optimal solution above a quadratic speedup over random guessing for up to 42 qubits and p = 10 layers. Experiments on quantum hardware platforms such as IonQ Harmony, Aria, Rigetti Aspen-M-3, and IBM Brisbane show that IonQ Aria yields the best overlap with the ideal probability distribution. Additionally, the study shows that cross-platform TL is possible using the D-Wave Advantage quantum annealer with BPP parameters, achieving improved performance for MIS problems with up to 170 qubits. The results indicate that QAOA parameters can generalize well across different COPs and annealing protocols. The study also highlights the benefits of TL in reducing the computational resources required for classical optimization, particularly for large problems. The findings suggest that TL can be a valuable tool for improving the efficiency and effectiveness of quantum algorithms in solving complex optimization problems.