The reliability and consistent performance of photonic machine-learning form analyzers have proven to be a major challenge, because credibility-dependent measurements, optical noise, dynamic signal variation, and performance evaluation by experts have become critical issues. This research paper introduces a reliability-conscious multi-criteria decision model conducted in an Intuitionistic Fuzzy Frank Z-Number (\(\textrm{IFFZN}\)) environment, in which each evaluation is represented as \(\Omega = \{(\mu , r_\mu ), (\nu , r_\nu )\}\), an independent encoding of truth-membership, falsity-membership, hesitation, and their associated reliability degrees. Compared with traditional fuzzy and intuitionistic systems, the semantic representation of performance behavior and reliability is two-fold in IFFZN, avoiding information distortion during aggregation. The main innovation is that reliability is decoupled from the main evaluation data without losing its interactive effect via Frank operational laws, allowing effective decision-making under incomplete, imprecise, and credibility-variant observations intrinsic to intelligent photonic analytical systems. Six Frank norm-based aggregation operators are developed: \(\textrm{IFFZNWA}\), \(\textrm{IFFZNWG}\), \(\textrm{IFFZNOWA}\), \(\textrm{IFFZNOWG}\), \(\textrm{IFFZNHWA}\), and \(\textrm{IFFZNHWG}\), with mathematical properties of idempotency, monotonicity, and boundedness formally established. The framework is applied to a synthetic case study evaluating four photonic ML analyzer alternatives across four reliability-oriented attributes, assessed by three domain experts using synthetic sensor data calibrated to photonic ML scenarios. This results in a more realistic representation of the cognitive behavior of domain experts and produces rankings that remain consistent under confidence perturbations and heterogeneous data conditions. A comparative investigation with classical WASPAS shows deterministic schemes cannot encode credibility-aware uncertainty and inter-criteria coupling essential for high-precision photonic evaluation. The outcomes provide a scalable, analytically robust reliability assessment protocol for next-generation photonic ML form analyzers and a transferable uncertainty-conscious decision infrastructure for other smart optical and AI-based diagnostic environments requiring reliable performance measurement. This work establishes a new research direction in confidence- preserving computational intelligence, advancing the theoretical basis of \(\textrm{IFFZN}\)-based multi-criteria decision-making by combining reliability semantics and advanced aggregation theory. The numerical validation is conducted on synthetic data; hardware validation with real photonic ML systems is identified as essential future work.
