In a pervasive multimodal multimedia computing system, the user can continue working on a computing task anytime and anywhere using forms of modality that suit his context. Similarly, the media supporting the chosen modality are selected based on their availability and user’s context. In this paper, we present the infrastructure supporting the migration of a visually-impaired user’s task in a pervasive multimodal multimedia computing environment. Using user’s preferences which quantify user’s satisfaction, we derive the user’s task feasible configuration. The heart of this work is the machine learning-derived training to acquire knowledge leading to configuration optimization. Data validation is presented through scenario simulations and design specification. This work is our continuing contribution to advance research on making informatics more accessible to handicapped users.