In the volatile sphere of copyright, portfolio optimization presents a formidable challenge. Traditional methods often fail to keep pace with the swift market shifts. However, machine learning techniques are emerging as a powerful solution to enhance copyright portfolio performance. These algorithms interpret vast pools of data to identify correlat