The article is devoted to the issues of innovative development of high-tech industry and assessment of digital maturity of economic systems. The aim of the research is to develop theoretical and applied approaches to assessing the effectiveness of high-tech industry based on the methods of innovation economics and machine learning (ML) in the context of analyzing and increasing the digital maturity of enterprises and ecosystems. Methods of system analysis, economic and statistical analysis (including analysis of GRP dynamics, labor productivity and innovation activity), methods for evaluating the effectiveness of digital assets (LTV, CAC), as well as structural modeling of business processes were used. The article examines the evolution of approaches to innovation management in industry using the example of the Republic of Crimea. In the first part of the work, an analysis of the current state of the region’s high-tech sector was carried out using data economy metrics, and a gap in the level of digital maturity between Crimean enterprises and all-Russian leaders was revealed. The second part of the study examines the architectures of predictive pricing systems and models of decentralized interaction (Web3). It has been established that the key barrier remains the low level of gross value added in knowledge-intensive industries, while investments in fixed assets are growing. It is shown that the targeted implementation of ML algorithms in inventory and logistics management can increase operational efficiency by 12–15% without significant capital costs. A modified balanced scorecard (Digital BSC) has been developed and substantiated, adapted to assess the digital maturity of industrial enterprises in the region. A methodology for assessing the digital maturity of an ecosystem is proposed, taking into account coevolutionary processes. The proposed Digital BSC system, which includes, along with financial results, Data Quality Index and staff innovation sensitivity metrics, makes it possible to quantify the level of digital maturity and identify “bottlenecks” in data management. The necessity of the transition from extensive digitalization to the purposeful development of competencies in the field of Data Science and industrial design as a basis for increasing the digital maturity of ecosystems is substantiated. For the Crimean industry, a decomposition of innovation processes was carried out at the level of “data → forecast → solution”, imbalances in the structure of investment in R&D were identified, and a mechanism for the “digital twin” of the regional industrial complex was proposed for testing government support measures.