Fisheries and its value added products contributes substantially in the socio-economic of developing countries including Tanzania. Researches shows that fisheries sector contributes 4.7% and 2.4% of the Gross Domestic Product (GDP) of Kenya and Tanzania respectively. Despite its huge contribution to socio-economic of the country, the Tanzania fisheries stakeholders remain challenged with limited access of fisheries information, knowledge, skills and new technologies. This challenges hinders the fisheries sector development and reduces income to stakeholders as well as the Government. This study investigated the fisheries information collecting and distribution among fisheries stakeholders in Mara and Mwanza regions of Tanzania. The study examined the channels owned and used by fisheries stakeholders to gather and disseminate fisheries information. Data were collected by administering a survey in four (4) districts purposively selected from the two regions and 400 respondents randomly selected was involved. The data were analyzed using python panda library and presented using bar and pie charts. Using the collected data, channel dissemination effectiveness probability of the six channels (short Message services, Cellular phone call, Television, Radio, mobile application, and Website) were calculated and comprehensive analysis performed using python plotly library. Furthermore, the study developed a multi channel fisheries information management system architectural framework and a participation reputation game based incentive mechanism namely EPRIGM to encourage the fisheries stakeholders donate truthful information and feedback. We modeled and simulated the dynamics of stakeholder’s strategy selection using replicator dynamic concept and derive the evolutionary stable strategies for the stakeholders. Results revealed that there is no single channel application that fits all stakeholders and that EPRIGM ensures truthful and honest stakeholders participation in gathering and disseminating fisheries information. In this study, we considered only seven parameters, namely channel coverage, listening ratio, watching ratio, channel access, average access time, information usefulness, and information sharing, in calculating channel effectiveness probability. Lastly, the empirical results of EPRIGM simulation revealed that all information users and information providers will choose honest strategy to capitalize on their earnings. We do recommend further studies to consider more factors like channel carrying capacity and channel costs in calculating channel effectiveness probability and consider application of EPRIGM in other domain of activities.