The presentation titled "Ionospheric Data Filters: A new method for Ionogram Scaling" (Presented in the DISPEC WP2 Review Meeting, held in July 2025) summarizes the design, development, and validation of an advanced method for automatic ionogram scaling using AI/ML techniques. The core goal is to improve the accuracy and confidence in determining key ionospheric parameters (especially foF2 and hmF2) from Digisonde data. Key points include: Identifying the need to correct ARTIST outputs using hybrid time series and confidence levels. Developing a new DISPEC autoscaling algorithm based on clustering, curve fitting, and feedforward neural networks. Applying this method to detect E, Es, and F layers, and accurately estimate foF2 and hmF2. Evaluating performance on diverse ionospheric conditions, including MSTIDs, multi-reflections, and disturbed periods (e.g., Hunga volcano eruption). Validation showed a deviation from manual scaling of the size of the instrument resolution. Planned improvements include broader station testing, confidence scoring, hybrid time series filtering, and HLDP (High-Level Data Product) derivation.