This paper presents a comprehensive approach to optimizing furniture design processes and facilitating wood identification through the development of a mobile application. The application is designed to provide users with intuitive tools and resources for informed decision-making in furniture design and wood selection. The first component of the mobile application focuses on optimizing furniture design through furniture size and quality consideration. This encompasses the creation of an intuitive user interface that enables users to specify their furniture design preferences, including material selection criteria. A comprehensive wood database is meticulously curated, furnishing users with comprehensive details on wood sizes, qualities, and thicknesses, thereby aiding in material selection and waste reduction initiatives. Algorithms are employed to recommend optimal wood sizes, considering factors such as material selection, waste reduction, and cost analysis. Visual representation tools are included, allowing users to preview furniture designs with different wood options, facilitating informed decision-making. Furthermore, feedback mechanisms have been integrated to enable users to provide feedback on recommended wood sizes and qualities, thereby facilitating continuous enhancement and refinement of recommendations. The second component aims to identify the type of wood by its appearance. Image processing features have been developed to precisely extract visual characteristics from wood sample images, thereby aiding in the identification of wood species. Machine learning models are trained to classify wood species based on visual characteristics obtained from images, thereby enhancing the precision of identification. User interaction tools have been designed to ensure ease of use and accessibility for users in capturing and submitting wood sample images. Within the application, educational resources and materials are provided to help users learn about different wood species, aiding their understanding and selection process.