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Stone Inscriptions Are One Of The Most Significant Sources Of Historical, Cultural, And Linguistic Knowledge, Especially For Understanding Ancient Civilizations. In Maharashtra, Stone Inscriptions Written In Early Forms Of The Marathi Script Provide Invaluable Insights Into Socio-political, Religious, And Cultural Developments Of Their Time. However, The Manual Study Of Such Inscriptions Is Challenging Due To Natural Weathering, Erosion, Script Variations, And The Complexity Of Ancient Writing Styles. In Recent Years, Optical Character Recognition (OCR) Has Emerged As A Powerful Tool To Digitize And Analyze Ancient Scripts, Enabling Automated Recognition And Preservation Of Historical Texts. This Paper Presents A Comprehensive Survey Of Existing Methods And Approaches For Ancient Marathi Script Recognition From Stone Inscriptions. The Study Reviews Key Preprocessing Techniques Such As Image Enhancement, Noise Reduction, And Segmentation; Feature Extraction Methods Including Structural, Statistical, And Deep Learning-based Approaches; And Classification Models Ranging From Traditional Machine Learning To Modern Convolutional Neural Networks. Challenges Such As Degraded Surfaces, Broken Characters, And Script Variability Are Discussed Alongside Potential Solutions. The Survey Also Highlights Future Research Directions, Particularly The Integration Of Deep Learning, Transfer Learning, And Multimodal Analysis For Improved Accuracy. By Compiling And Analyzing Existing Research, This Work Aims To Provide A Foundation For Developing Robust OCR Systems Tailored To Ancient Marathi Stone Inscriptions, Thereby Contributing To Digital Preservation And Historical Scholarship.