This study presents a new Python-based novel framework, in a distributed computing environment for multidisciplinary design optimization (MDO) called DELWARX. DELWARX also includes a transonic flutter analysis approach that is computationally very efficient, yet accurate enough for conceptual design and optimization studies. This transonic flutter analysis approach is designed for large aspect-ratio wings and attached flow. The framework employs particle swarm optimization with penalty functions for exploring optimal Transonic Truss Braced Wing (TTBW) aircraft design, similar to the Boeing 737-800 type of mission with a cruise Mach of 0.8, a range of 3115 n miles, and 162 passengers, with two different objective functions, the fuel weight and the maximum take-off gross weight, while satisfying all the required constraints. Proper memory management is applied to effectively address memory-related issues, which are often a limiting factor in distributed computing. The parallel implementation in MDO using 60 processors allowed a reduction in the wall-clock time by 96% which is around 24 times faster than the optimization using a single processor. The results include a comparison of the TTBW designs for the medium-range missions with and without the flutter constraint. Importantly, the framework achieves extremely low computation times due to its parallel optimization capability, retains all the previous functionalities of the previous Virginia Tech MDO framework, and replaces the previously employed linear flutter analysis with a more accurate nonlinear transonic flutter computation. These features of DELWARX are expected to facilitate a more accurate MDO study for innovative transport aircraft configurations operating in the transonic flight regime. High-fidelity CFD simulation is performed to verify the result obtained from extended Strip theory based aerodynamic analysis method. An approach is presented to develop a deep neural network (DNN)-based surrogate model for fast and accurate prediction of flutter constraints in multidisciplinary design optimization (MDO) of Transonic Truss Braced Wing (TTBW) aircraft in the transonic region. The integration of the surrogate model in the MDO framework shows lower computation times than the MDO with nonlinear flutter analysis. The developed surrogate models can predict the optimum design. The wall-clock time of the design analysis method was reduced by 1500 times as compared to the result implemented in the previous framework, DELWARX. / Doctor of Philosophy / The current study presents DELWARX, a novel Python-based framework specifically engineered for the optimization of aircraft designs, with a primary focus on enhancing the performance of aircraft wings under transonic conditions (speeds approaching the speed of sound). This advancement is particularly pertinent for aircraft with a mission analogous to the Boeing 737-800, which necessitates a harmonious balance between speed, range, passenger capacity, and fuel efficiency. A salient feature of DELWARX is its adeptness in analyzing and optimizing wing flutter, a critical issue where wings may experience hazardous vibrations at certain velocities. This is particularly vital for wings characterized by a high aspect ratio (wings that are long and narrow), presenting a substantial challenge in the domain of aircraft design. DELWARX surpasses preceding methodologies by implementing a sophisticated computational technique known as particle swarm optimization, analogous to the collective movement observed in bird flocks, integrated with penalty functions that serve to exclude design solutions that fail to meet predefined standards. This approach is akin to navigating through a maze with specific pathways rendered inaccessible due to certain constraints. The efficiency of DELWARX is markedly enhanced by its ability to distribute computational tasks across 60 processors, achieving a computation speed that is 24 times faster than that of a single-processor operation. This distribution results in a significant reduction of overall computation time by 96%, representing a substantial advancement in processing efficiency. Further, DELWARX introduces an enhanced level of precision in its operations. It supplants former methods of flutter analysis with a more sophisticated, nonlinear approach tailored for transonic speeds. Consequently, the framework's predictions and optimization strategies for aircraft wing designs are imbued with increased reliability and accuracy. Moreover, DELWARX also integrates a Deep Neural Network (DNN), an advanced form of artificial intelligence, to swiftly and precisely predict flutter constraints. This integration manifests as a highly intelligent system capable of instantaneously estimating the performance of various designs, thereby expediting the optimization process. DELWARX employs high-fidelity Computational Fluid Dynamics (CFD) simulations to verify its findings. These simulations utilize intricate models to simulate the aerodynamics of air flow over aircraft wings, thereby ensuring that the optimized designs are not only theoretically sound but also pragmatically effective. In conclusion, DELWARX represents a significant leap in the field of multidisciplinary design optimization. It offers a robust and efficient tool for the design of aircraft wings, especially in the context of transonic flight. This framework heralds a new era in the optimization of aircraft designs, enabling more innovative and efficient solutions in the aerospace industry.