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Stone Inscriptions Are A Vital Source Of Historical Knowledge, Particularly In The Tamil Civilization, Which Has Preserved Cultural, Linguistic, And Political Information Through Ancient Scripts. However, These Inscriptions Face Challenges Such As Erosion, Complex Letter Forms, And Limited Accessibility For Modern Readers. This Study Proposes A System For Automatic Recognition And Speech Synthesis Of Ancient Tamil Characters From Stone Inscriptions Using Advanced Computational Techniques. The Methodology Involves Image Preprocessing To Enhance Inscription Clarity, Segmentation To Isolate Characters, And Recognition Using Deep Learning Models Such As Convolutional Neural Networks (CNNs) Trained On Ancient Tamil Script Datasets. Once The Characters Are Identified, They Are Mapped To Their Modern Tamil Equivalents And Converted Into Speechable Audio Using Text-to-Speech (TTS) Technology. This Approach Not Only Preserves Ancient Tamil Heritage But Also Makes Inscriptions More Accessible To Historians, Researchers, And The General Public. The Proposed System Contributes To The Fields Of Optical Character Recognition (OCR), Digital Preservation, And Cultural Informatics, Ensuring That Ancient Tamil Inscriptions Are Both Digitally Archived And Audibly Experienced By Future Generations.