This study examines the shortcomings of Mexico’s natural gas network in structural, regulatory, and tariff dimensions. Using a mixed-methods approach that combines documentary review, technical auditing, and field case studies, and drawing on authoritative data from the CRE, SENER, and CENAGAS, the research identifies four principal challenges: high dependence on imports, persistent regulatory ambiguities, tariff distortions, and inaccuracies in pricing mechanisms. To address these deficiencies, the study proposes an integrated techno-regulatory framework encompassing strategic pipeline expansion in underserved regions, the development of underground gas-storage facilities, enhanced nodal interconnections, system-wide digitalization, and legal reforms with binding minimum performance standards. In addition, the chapter outlines a research agenda for the experimental implementation of AI-powered digital tutors based on Large Language Models (LLMs). These tools are intended to support regulators, operators, and users in tasks related to tariff management, regulatory compliance, and stakeholder training. Although the AI components remain at a conceptual and pilot stage, the overall framework offers a replicable roadmap for strengthening Mexico’s natural gas sector through evidence-based, technically feasible, and progressively scalable measures. The proposed model aims to enhance system reliability, reduce regional disparities, and empower end-users by reinforcing infrastructure, harmonizing regulations, and increasing transparency in price-setting mechanisms. Preliminary estimates of potential benefits, such as tariff error reduction and regional reliability improvements are incorporated into the assessment. Scenario validation against international analogues from Germany, Chile, and Canada further confirms the practicality and scalability of the proposed interventions.