In the volatile sphere of copyright, portfolio optimization presents a formidable challenge. Traditional methods often struggle to keep pace with the dynamic market shifts. However, machine learning techniques are emerging as a promising solution to maximize copyright portfolio performance. These algorithms interpret vast information sets to identi