Innovative data collection programs are required to monitor scalefish fisheries species and inform robust stock assessments in Tasmania. Accurate fish length data is essential for assessing fish stocks and ensuring sustainable fisheries management. We compare various methods for collecting fish length data, including fishery-independent stereo-video Baited Remote Underwater Video (BRUVs) and Remotely Operated Vehicles (ROVs) surveys, and fishery-dependent electronic measuring boards combined with cameras in commercial fish markets and onboard scalefish fishery vessels. Each method provides unique advantages, and biases, for capturing length data for different species, in various environments, from natural habitats to commercial operations. We will also assess integrating these various data sources into length-based stock assessments, which play a critical role in estimating stock status in data-poor fisheries. Additionally, we are developing cost-efficient video processing techniques powered by artificial intelligence (AI) and machine learning (ML) models, trained for Tasmanian scalefish fisheries species. AI-driven automation is streamlining processing of large datasets, reducing manual effort while improving accuracy and consistency. Combining diverse data collection methods with cutting-edge technology will enhance length-based assessment precision and support informed management decisions. This framework will contribute to long-term sustainability by enabling better monitoring of various scalefish fisheries and guiding harvest strategies.