A recent study on entropy estimation of Amharic language showed that its 16-bitrnrepresentation in Universal Transformation Format (UTF-8) very high as compared tornthe entropy of the language. The study showed a minimum of 1.074 ð‘ð‘–ð‘¡ð‘ /ð‘ ð‘¦ð‘šð‘ð‘œð‘™ andrna maximum of 7.981 ð‘ð‘–ð‘¡ð‘ /ð‘ ð‘¦ð‘šð‘ð‘œð‘™ can be sufficient for transmission of text sourcesrnwritten in Amharic through telecom networks. In digital communication, the sourcernencoding operation produces a compressed representation of an information source forrnefficient utilization of communication resources like bandwidth and energy. Practicalrnsource encoding approaches in text compression use Statistical Language Modelsrn(SLMs) based on Markov process to model redundancies exhibited in a language. rnThe Prediction by Partial Match (PPM) context-modeling algorithm is capable of highrncompression rates and is well suited for multiple alphabet sources like textual data.rnPPM adaptively combines different order Markov models to capture dependenciesrnbetween successive symbols in a text. In this thesis, the PPM algorithm is used to showrnthe advantages gained by context-modeling techniques in Amharic text sourcernencoding and demonstrate how close practical compression gets to estimated entropyrnof Amharic language. rnTwo Versions of the PPM algorithm; namely PPMC and PPMD were used to modelrnand encode eight source files written in Amharic. It is shown that the optimum orderrnfor efficient encoding is order-3 and it is possible to achieve an average ofrn84.2% reduction in file size. Using both algorithms, an average compression rate ofrn3.3 ð‘ð‘–ð‘¡ð‘ /ð‘ ð‘¦ð‘šð‘ð‘œð‘™ is attainable for source encoding and storage applications. ModelingrnAmharic text sources using context models in general and PPM in particular can helprnto maximize efficiency in communication networks by reducing the average numberrnof bits required for coding text sources